refactoring training spc setup and benchmarking

.env.sweep.example

@@ -3,7 +3,7 @@
 # Required for wandb runs and sweep agent workers.
 WANDB_API_KEY=
 WANDB_ENTITY=
-WANDB_PROJECT=phantom-pricing
+WANDB_PROJECT=capstone
 
 # Required for private repo bootstrap workers.
 GITHUB_TOKEN=
@@ -16,3 +16,7 @@ GITHUB_TOKEN=
 # AGENT_COUNT=0
 # AGENT_LOOP=1
 # RETRY_SECONDS=20
+
+# Optional local benchmark defaults.
+# LOCAL_BENCHMARK_ARGS=--tiers static,surge,linear,qtable,ppo --alpha-values 0.0,0.3 --episodes 3 --total-timesteps 3000 --max-steps 40 --device cpu
+# BENCHMARK_AGENT_ARGS=--tiers static,surge,linear,qtable,ppo --alpha-values 0.0,0.3,0.6 --episodes 5

.gitignore

@@ -68,6 +68,7 @@ sim/case/thesis_simplified/runs*/
 
 # model binaries
 engine/models/*.zip
+engine/studies/results/*
 *.zip
 
 # wandb local state

Makefile

@@ -13,9 +13,11 @@ NX        := npx nx
 SWEEP_ENV_FILE ?= .env.sweep
 
 WANDB_ENTITY ?=
-WANDB_PROJECT ?= phantom-pricing
+WANDB_PROJECT ?= capstone
 SWEEP_ID ?=
 LOCAL_TRAIN_ARGS ?= --algo ppo --total-timesteps 50000
+LOCAL_BENCHMARK_ARGS ?= --tiers static,surge,linear,qtable,ppo --alpha-values 0.0,0.3 --episodes 3 --total-timesteps 3000 --max-steps 40 --device cpu
+BENCHMARK_AGENT_ARGS ?=
 AGENT_COUNT ?= 0
 
 REPO_URL ?=
@@ -36,7 +38,7 @@ SWEEP_ENV_LOAD = set -a; [ -f "$(SWEEP_ENV_FILE)" ] && . "$(SWEEP_ENV_FILE)" ||
 
 .PHONY: help
 help:
-	@echo "pdf.build pdf.watch pdf.clean pdf.genpop pdf.genpop.watch | test.backend test.e2e test.all | web.dev | install | train | train.agent | train.bootstrap | train.tpu.pod | train.tpu.vm | train.tpu.vm.sweep | stats.lines"
+	@echo "pdf.build pdf.watch pdf.clean pdf.genpop pdf.genpop.watch | test.backend test.e2e test.all | web.dev | install | train | benchmark | benchmark.agent | train.agent | train.bootstrap | train.tpu.pod | train.tpu.vm | train.tpu.vm.sweep | stats.lines"
 	@echo "backend.server backend.provider backend.worker | platform.up platform.down platform.logs | docker.train.publish"
 	@echo ""
 	@echo "Build general public version:"
@@ -45,6 +47,9 @@ help:
 	@echo "Local wandb run:"
 	@echo "  make train LOCAL_TRAIN_ARGS='--algo ppo --total-timesteps 50000'"
 	@echo ""
+	@echo "Local benchmark run:"
+	@echo "  make benchmark LOCAL_BENCHMARK_ARGS='--tiers static,surge,linear --alpha-values 0.0,0.3 --episodes 3 --no-wandb'"
+	@echo ""
 	@echo "Local sweep agent from this repo:"
 	@echo "  make train.agent SWEEP_ID=entity/project/id AGENT_COUNT=5"
 	@echo ""
@@ -104,6 +109,14 @@ install:
 train:
 	@WANDB_ENTITY="$(WANDB_ENTITY)" WANDB_PROJECT="$(WANDB_PROJECT)" SWEEP_ENV_FILE="$(SWEEP_ENV_FILE)" LOCAL_TRAIN_ARGS="$(LOCAL_TRAIN_ARGS)" $(NX) run research:train
 
+.PHONY: benchmark
+benchmark:
+	@WANDB_ENTITY="$(WANDB_ENTITY)" WANDB_PROJECT="$(WANDB_PROJECT)" SWEEP_ENV_FILE="$(SWEEP_ENV_FILE)" LOCAL_BENCHMARK_ARGS="$(LOCAL_BENCHMARK_ARGS)" $(NX) run research:benchmark
+
+.PHONY: benchmark.agent
+benchmark.agent:
+	@WANDB_ENTITY="$(WANDB_ENTITY)" WANDB_PROJECT="$(WANDB_PROJECT)" SWEEP_ENV_FILE="$(SWEEP_ENV_FILE)" SWEEP_ID="$(SWEEP_ID)" AGENT_COUNT="$(AGENT_COUNT)" BENCHMARK_AGENT_ARGS="$(BENCHMARK_AGENT_ARGS)" $(NX) run research:benchmark-agent
+
 .PHONY: train.agent
 train.agent:
 	@WANDB_ENTITY="$(WANDB_ENTITY)" WANDB_PROJECT="$(WANDB_PROJECT)" SWEEP_ENV_FILE="$(SWEEP_ENV_FILE)" SWEEP_ID="$(SWEEP_ID)" AGENT_COUNT="$(AGENT_COUNT)" $(NX) run research:train-agent

engine/backends/__init__.py

@@ -0,0 +1 @@
+__all__ = ["evaluate", "make_env", "train_jax_backend", "train_qtable", "train_sb3"]

engine/backends/common.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+from typing import Any, Mapping
+
+import numpy as np
+
+
+def make_env(cfg: Mapping[str, Any]):
+    from gymnasium.wrappers import FlattenObservation
+
+    from ..lib.wrappers import EconomicMetricsWrapper
+    from ..wrapper import PHANTOM
+
+    env = PHANTOM(
+        n_products=int(cfg["n_products"]),
+        alpha=float(cfg["alpha"]),
+        N=int(cfg["N"]),
+        price_bounds=(float(cfg["price_low"]), float(cfg["price_high"])),
+        lambda_coi=float(cfg["lambda_coi"]),
+        robust_radius=float(cfg["robust_radius"]),
+        robust_points=int(cfg["robust_points"]),
+        info_value=float(cfg["info_value"]),
+        action_levels=int(cfg["action_levels"]),
+        action_scale_low=float(cfg["action_scale_low"]),
+        action_scale_high=float(cfg["action_scale_high"]),
+        max_steps=int(cfg.get("max_steps", 100)),
+        margin_floor=float(cfg.get("margin_floor", 0.05)),
+        margin_floor_patience=int(cfg.get("margin_floor_patience", 5)),
+        render_mode=None,
+    )
+    env = EconomicMetricsWrapper(env)
+    return FlattenObservation(env)
+
+
+def _action(agent: Any, obs: Any, deterministic: bool = True):
+    out = agent.predict(obs, deterministic=deterministic)
+    action = out[0] if isinstance(out, tuple) else out
+    if isinstance(action, np.ndarray) and action.size == 1:
+        return int(action.reshape(-1)[0])
+    return action
+
+
+def evaluate(agent: Any, env: Any, episodes: int) -> dict[str, float]:
+    rewards: list[float] = []
+    revenues: list[float] = []
+    margins: list[float] = []
+    coi_levels: list[float] = []
+
+    for _ in range(int(episodes)):
+        obs, _ = env.reset()
+        done = False
+        ep_reward = 0.0
+        ep_revenue = 0.0
+        ep_margin = 0.0
+        ep_coi = 0.0
+        steps = 0
+
+        while not done:
+            obs, reward, term, trunc, info = env.step(_action(agent, obs, True))
+            done = bool(term or trunc)
+            econ = info.get("economics", {})
+            ep_reward += float(reward)
+            ep_revenue += float(econ.get("revenue", info.get("revenue", 0.0)))
+            ep_margin += float(econ.get("margin", 0.0))
+            ep_coi += float(econ.get("coi_level", 0.0))
+            steps += 1
+
+        rewards.append(ep_reward)
+        revenues.append(ep_revenue)
+        denom = max(steps, 1)
+        margins.append(ep_margin / denom)
+        coi_levels.append(ep_coi / denom)
+
+    return {
+        "eval/reward_mean": float(np.mean(rewards)) if rewards else 0.0,
+        "eval/reward_std": float(np.std(rewards)) if rewards else 0.0,
+        "eval/revenue_mean": float(np.mean(revenues)) if revenues else 0.0,
+        "eval/revenue_std": float(np.std(revenues)) if revenues else 0.0,
+        "eval/margin_mean": float(np.mean(margins)) if margins else 0.0,
+        "eval/coi_level_mean": float(np.mean(coi_levels)) if coi_levels else 0.0,
+    }

engine/backends/jax.py

@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from typing import Any, Mapping
+
+from ..jax import JAX_AVAILABLE
+
+
+def train_jax_backend(
+    cfg: Mapping[str, Any],
+) -> tuple[dict[str, Any], dict[str, float | int | str]]:
+    if not JAX_AVAILABLE:
+        raise ImportError(
+            "JAX backend requested but JAX is not installed. "
+            "Install engine/jax/requirements.txt and jax[tpu] for TPU runs."
+        )
+    from ..jax.train import train_jax
+
+    return train_jax(dict(cfg))

engine/backends/qtable.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from typing import Any, Mapping
+
+import numpy as np
+
+from .common import evaluate, make_env
+
+
+def train_qtable(cfg: Mapping[str, Any]) -> tuple[object, dict[str, float | int]]:
+    from ..lib.discrete import EventQTable
+
+    np.random.seed(int(cfg["seed"]))
+    env = make_env(cfg)
+    eval_env = make_env(cfg)
+    agent = EventQTable(
+        env.action_space.n,
+        int(cfg["n_products"]),
+        (float(cfg["price_low"]), float(cfg["price_high"])),
+        lr=float(cfg["q_lr"]),
+        gamma=float(cfg["gamma"]),
+        n_bins=int(cfg["q_bins"]),
+    )
+
+    total_reward = 0.0
+    total_revenue = 0.0
+    steps = 0
+    epsilon = float(cfg["eps_start"])
+    obs, _ = env.reset(seed=int(cfg["seed"]))
+
+    for _ in range(int(cfg["total_timesteps"])):
+        action, state = agent.act(obs, epsilon)
+        nxt, reward, term, trunc, info = env.step(action)
+        done = bool(term or trunc)
+        agent.update(state, action, float(reward), agent.encode(nxt), done)
+
+        total_reward += float(reward)
+        total_revenue += float(info.get("economics", {}).get("revenue", 0.0))
+        steps += 1
+        epsilon = max(float(cfg["eps_end"]), epsilon * float(cfg["eps_decay"]))
+        obs = env.reset()[0] if done else nxt
+
+    metrics: dict[str, float | int] = {
+        "train/reward_mean": total_reward / max(steps, 1),
+        "train/revenue_mean": total_revenue / max(steps, 1),
+        "train/epsilon": float(epsilon),
+        "train/global_step": int(cfg["total_timesteps"]),
+    }
+    metrics.update(evaluate(agent, eval_env, int(cfg["eval_episodes"])))
+
+    env.close()
+    eval_env.close()
+    return agent, metrics

engine/backends/sb3.py

@@ -0,0 +1,228 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, Mapping
+
+from ..lib.callbacks import CheckpointArtifactCallback, MetricsCallback
+from ..telemetry.wandb import get_wandb_module
+from ..wandb_checkpoint import checkpoint_artifact_name, download_latest_checkpoint
+from .common import evaluate, make_env
+
+
+def _net_arch(name: Any) -> list[int]:
+    presets = {
+        "tiny": [32, 32],
+        "small": [64, 64],
+        "medium": [128, 128],
+        "large": [256, 256],
+    }
+    if isinstance(name, (list, tuple)):
+        return [int(v) for v in name]
+    raw = str(name).lower().strip()
+    if raw in presets:
+        return presets[raw]
+    if "x" in raw:
+        try:
+            parsed = [int(v) for v in raw.split("x") if v]
+            return parsed if parsed else presets["small"]
+        except ValueError:
+            return presets["small"]
+    return presets["small"]
+
+
+def _activation(name: Any):
+    try:
+        import torch.nn as nn
+    except ImportError:
+        return None
+    return {
+        "relu": nn.ReLU,
+        "tanh": nn.Tanh,
+        "elu": nn.ELU,
+        "leaky_relu": nn.LeakyReLU,
+    }.get(str(name).lower().strip(), nn.ReLU)
+
+
+def _policy_kwargs(cfg: Mapping[str, Any]) -> dict[str, Any]:
+    kwargs: dict[str, Any] = {"net_arch": _net_arch(cfg.get("arch", "small"))}
+    activation = _activation(cfg.get("activation", "relu"))
+    if activation is not None:
+        kwargs["activation_fn"] = activation
+    return kwargs
+
+
+def _sb3_model_cls(algo: str):
+    try:
+        from stable_baselines3 import A2C, DQN, PPO
+    except ImportError as exc:
+        raise ImportError("stable-baselines3 is required for SB3 algorithms") from exc
+
+    if algo == "ppo":
+        return PPO
+    if algo == "a2c":
+        return A2C
+    if algo == "dqn":
+        return DQN
+    raise ValueError(f"unsupported algo '{algo}'")
+
+
+def build_model(cfg: Mapping[str, Any], env: Any):
+    try:
+        from stable_baselines3 import A2C, DQN, PPO
+    except ImportError as exc:
+        raise ImportError("stable-baselines3 is required for SB3 algorithms") from exc
+
+    algo = str(cfg["algo"])
+    policy_kwargs = _policy_kwargs(cfg)
+    device = str(cfg.get("device", "auto"))
+    seed = int(cfg["seed"])
+
+    if algo == "sac":
+        raise ValueError("sac is not supported with the discrete core env")
+    if algo == "ppo":
+        return PPO(
+            "MlpPolicy",
+            env,
+            verbose=1,
+            device=device,
+            policy_kwargs=policy_kwargs,
+            seed=seed,
+            learning_rate=float(cfg["learning_rate"]),
+            n_steps=int(cfg["n_steps"]),
+            batch_size=int(cfg["batch_size"]),
+            n_epochs=int(cfg["n_epochs"]),
+            gamma=float(cfg["gamma"]),
+            gae_lambda=float(cfg["gae_lambda"]),
+            clip_range=float(cfg["clip_range"]),
+            ent_coef=float(cfg["ent_coef"]),
+        )
+    if algo == "a2c":
+        return A2C(
+            "MlpPolicy",
+            env,
+            verbose=1,
+            device=device,
+            policy_kwargs=policy_kwargs,
+            seed=seed,
+            learning_rate=float(cfg["learning_rate"]),
+            n_steps=max(5, int(cfg["n_steps"]) // 32),
+            gamma=float(cfg["gamma"]),
+            gae_lambda=float(cfg["gae_lambda"]),
+            ent_coef=float(cfg["ent_coef"]),
+        )
+    if algo == "dqn":
+        return DQN(
+            "MlpPolicy",
+            env,
+            verbose=1,
+            device=device,
+            policy_kwargs=policy_kwargs,
+            seed=seed,
+            learning_rate=float(cfg["learning_rate"]),
+            buffer_size=int(cfg["buffer_size"]),
+            batch_size=int(cfg["batch_size"]),
+            gamma=float(cfg["gamma"]),
+            train_freq=int(cfg["train_freq"]),
+            learning_starts=int(cfg["learning_starts"]),
+            target_update_interval=int(cfg["target_update_interval"]),
+            exploration_fraction=float(cfg["exploration_fraction"]),
+            exploration_final_eps=float(cfg["exploration_final_eps"]),
+        )
+    raise ValueError(f"unsupported algo '{algo}'")
+
+
+def _maybe_resume_model(cfg: Mapping[str, Any], env: Any, model: Any):
+    wandb = get_wandb_module()
+    if wandb is None or wandb.run is None:
+        return model
+
+    sweep_id = getattr(wandb.run, "sweep_id", None)
+    artifact_name = checkpoint_artifact_name(cfg, backend="sb3", sweep_id=sweep_id)
+    checkpoint_file = f"phantom_{cfg['algo']}_checkpoint.zip"
+    restored = download_latest_checkpoint(artifact_name, file_name=checkpoint_file)
+    if restored is None:
+        return model
+
+    checkpoint_path, metadata = restored
+    resumed = _sb3_model_cls(str(cfg["algo"]).lower()).load(
+        checkpoint_path.as_posix(),
+        env=env,
+    )
+    resume_step = int(metadata.get("step", getattr(resumed, "num_timesteps", 0)))
+    resumed.num_timesteps = max(int(getattr(resumed, "num_timesteps", 0)), resume_step)
+    return resumed
+
+
+def train_sb3(cfg: Mapping[str, Any]) -> tuple[object, dict[str, float | int | str]]:
+    try:
+        from stable_baselines3.common.callbacks import EvalCallback
+        from stable_baselines3.common.monitor import Monitor
+    except ImportError as exc:
+        raise ImportError("stable-baselines3 is required for SB3 models") from exc
+
+    env = Monitor(make_env(cfg))
+    eval_env = Monitor(make_env(cfg))
+    model = build_model(cfg, env)
+
+    try:
+        import torch
+
+        print(
+            "PHANTOM_DEVICE: "
+            + json.dumps(
+                {
+                    "requested": str(cfg.get("device", "auto")),
+                    "torch_cuda_available": bool(torch.cuda.is_available()),
+                    "torch_device_count": int(torch.cuda.device_count()),
+                    "sb3_device": str(getattr(model, "device", "unknown")),
+                }
+            )
+        )
+    except Exception:
+        pass
+
+    model = _maybe_resume_model(cfg, env, model)
+
+    callbacks = [MetricsCallback(log_histograms=False, log_freq=int(cfg["log_freq"]))]
+    callbacks.append(
+        CheckpointArtifactCallback(
+            dict(cfg),
+            interval=int(cfg.get("checkpoint_interval", 10_000)),
+        )
+    )
+    callbacks.append(
+        EvalCallback(
+            eval_env,
+            eval_freq=int(cfg["eval_freq"]),
+            n_eval_episodes=int(cfg["eval_episodes"]),
+            deterministic=True,
+            verbose=0,
+        )
+    )
+
+    target_steps = int(cfg["total_timesteps"])
+    remaining_steps = max(0, target_steps - int(getattr(model, "num_timesteps", 0)))
+    if remaining_steps > 0:
+        model.learn(
+            total_timesteps=remaining_steps,
+            callback=callbacks,
+            reset_num_timesteps=False,
+        )
+
+    model_dir = Path(str(cfg["model_dir"]))
+    model_dir.mkdir(parents=True, exist_ok=True)
+    model_path = model_dir / f"phantom_{cfg['algo']}"
+    model.save(str(model_path))
+
+    metrics: dict[str, float | int | str] = evaluate(
+        model,
+        eval_env,
+        int(cfg["eval_episodes"]),
+    )
+    metrics["train/global_step"] = int(model.num_timesteps)
+    metrics["model/path"] = str(model_path.with_suffix(".zip"))
+
+    env.close()
+    eval_env.close()
+    return model, metrics

engine/benchmark.py

@@ -0,0 +1,456 @@
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from datetime import datetime, UTC
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from .lib.tiers import LinearElasticityPolicy, StaticPolicy, SurgePolicy
+from .spec import TrainSpec
+from .telemetry.wandb import get_wandb_module
+
+wandb = get_wandb_module()
+HAS_WANDB = wandb is not None
+
+
+def _parse_list(raw: str) -> list[str]:
+    return [x.strip().lower() for x in str(raw).split(",") if x.strip()]
+
+
+def _parse_float_list(raw: str) -> list[float]:
+    return [float(x.strip()) for x in str(raw).split(",") if x.strip()]
+
+
+def _truthy(value: str | bool | None) -> bool:
+    if isinstance(value, bool):
+        return value
+    if value is None:
+        return False
+    return str(value).strip().lower() in {"1", "true", "yes", "on"}
+
+
+def _action(policy, obs: np.ndarray):
+    out = policy.predict(obs, deterministic=True)
+    action = out[0] if isinstance(out, tuple) else out
+    if isinstance(action, np.ndarray) and action.size == 1:
+        return int(action.reshape(-1)[0])
+    return int(action)
+
+
+def _run_eval_episode(env, policy) -> dict:
+    obs, _ = env.reset()
+    done = False
+    total_reward = 0.0
+    total_revenue = 0.0
+    total_margin = 0.0
+    total_coi = 0.0
+    price_trace: list[float] = []
+    step_count = 0
+
+    while not done:
+        action = _action(policy, obs)
+        obs, reward, term, trunc, info = env.step(action)
+        done = bool(term or trunc)
+        econ = info.get("economics", {})
+        total_reward += float(reward)
+        total_revenue += float(econ.get("revenue", 0.0))
+        total_margin += float(econ.get("margin", 0.0))
+        total_coi += float(econ.get("coi_level", 0.0))
+        prices = np.asarray(info.get("prices", []), dtype=np.float32)
+        if prices.size > 0:
+            price_trace.append(float(np.mean(prices)))
+        step_count += 1
+
+    denom = max(step_count, 1)
+    return {
+        "reward": total_reward,
+        "revenue": total_revenue,
+        "mean_margin": total_margin / denom,
+        "mean_coi": total_coi / denom,
+        "price_trace": price_trace,
+    }
+
+
+def _build_tier(name: str, cfg: dict, alpha: float):
+    from .backends.common import make_env
+    from .backends.qtable import train_qtable
+    from .backends.sb3 import train_sb3
+
+    tier = name.lower().strip()
+    run_cfg = dict(cfg)
+    run_cfg["alpha"] = float(alpha)
+
+    if tier == "static":
+        return StaticPolicy(int(run_cfg["action_levels"]))
+
+    if tier == "surge":
+        return SurgePolicy(
+            n_actions=int(run_cfg["action_levels"]),
+            n_products=int(run_cfg["n_products"]),
+        )
+
+    if tier == "linear":
+        warmup_env = make_env(run_cfg)
+        policy = LinearElasticityPolicy(
+            n_actions=int(run_cfg["action_levels"]),
+            n_products=int(run_cfg["n_products"]),
+            price_low=float(run_cfg["price_low"]),
+            price_high=float(run_cfg["price_high"]),
+        )
+        policy.fit(
+            warmup_env,
+            warmup_steps=int(run_cfg.get("linear_warmup_steps", 800)),
+            seed=int(run_cfg["seed"]),
+        )
+        warmup_env.close()
+        return policy
+
+    if tier == "qtable":
+        agent, _ = train_qtable(run_cfg)
+        return agent
+
+    if tier in {"ppo", "a2c", "dqn"}:
+        run_cfg["algo"] = tier
+        agent, _ = train_sb3(run_cfg)
+        return agent
+
+    raise ValueError(f"unsupported tier '{name}'")
+
+
+def run_benchmark(
+    cfg: dict, tiers: list[str], alpha_values: list[float], n_episodes: int
+):
+    from .backends.common import make_env
+
+    rows: list[dict] = []
+    traces: list[dict] = []
+
+    for alpha in alpha_values:
+        for tier_name in tiers:
+            policy = _build_tier(tier_name, cfg, alpha)
+            env = make_env({**cfg, "alpha": float(alpha)})
+            eps = [_run_eval_episode(env, policy) for _ in range(int(n_episodes))]
+            env.close()
+
+            row = {
+                "tier": tier_name,
+                "alpha": float(alpha),
+                "episodes": int(n_episodes),
+                "mean_reward": float(np.mean([e["reward"] for e in eps])),
+                "mean_revenue": float(np.mean([e["revenue"] for e in eps])),
+                "mean_margin": float(np.mean([e["mean_margin"] for e in eps])),
+                "mean_coi": float(np.mean([e["mean_coi"] for e in eps])),
+                "std_revenue": float(np.std([e["revenue"] for e in eps])),
+            }
+            row["objective_score"] = (
+                row["mean_reward"]
+                + float(cfg.get("revenue_weight", 0.01)) * row["mean_revenue"]
+            )
+            rows.append(row)
+
+            max_len = max((len(e["price_trace"]) for e in eps), default=0)
+            step_means = []
+            for step in range(max_len):
+                vals = [
+                    e["price_trace"][step] for e in eps if step < len(e["price_trace"])
+                ]
+                step_means.append(float(np.mean(vals)) if vals else np.nan)
+            traces.append(
+                {
+                    "tier": tier_name,
+                    "alpha": float(alpha),
+                    "mean_price_trace": step_means,
+                }
+            )
+
+            if HAS_WANDB and wandb.run is not None:
+                wandb.log(
+                    {
+                        "study/alpha": float(alpha),
+                        "eval/reward_mean": row["mean_reward"],
+                        "eval/revenue_mean": row["mean_revenue"],
+                        "eval/margin_mean": row["mean_margin"],
+                        "objective/score": row["objective_score"],
+                        "objective/coi_preserved": row["mean_coi"],
+                    }
+                )
+
+    return pd.DataFrame(rows), traces
+
+
+def _plot_outputs(df: pd.DataFrame, traces: list[dict], out_dir: Path, stamp: str):
+    fig1 = plt.figure(figsize=(11, 4.5))
+    if "mode" in df.columns:
+        groups = sorted(df[["tier", "mode"]].drop_duplicates().values.tolist())
+        for tier, mode in groups:
+            sub = df[(df["tier"] == tier) & (df["mode"] == mode)].sort_values("alpha")
+            plt.plot(
+                sub["alpha"],
+                sub["mean_revenue"],
+                marker="o",
+                label=f"{tier}:{mode}",
+            )
+    else:
+        for tier in sorted(df["tier"].unique()):
+            sub = df[df["tier"] == tier].sort_values("alpha")
+            plt.plot(sub["alpha"], sub["mean_revenue"], marker="o", label=tier)
+    plt.xlabel("contamination alpha")
+    plt.ylabel("mean episode revenue")
+    plt.title("Revenue under contamination")
+    plt.grid(alpha=0.3)
+    plt.legend()
+    fig1.tight_layout()
+    rev_path = out_dir / f"benchmark_revenue_{stamp}.png"
+    fig1.savefig(rev_path, dpi=220)
+    plt.close(fig1)
+
+    fig2 = plt.figure(figsize=(11, 4.5))
+    if "mode" in df.columns:
+        groups = sorted(df[["tier", "mode"]].drop_duplicates().values.tolist())
+        for tier, mode in groups:
+            sub = df[(df["tier"] == tier) & (df["mode"] == mode)].sort_values("alpha")
+            plt.plot(
+                sub["alpha"],
+                sub["mean_coi"],
+                marker="s",
+                label=f"{tier}:{mode}",
+            )
+    else:
+        for tier in sorted(df["tier"].unique()):
+            sub = df[df["tier"] == tier].sort_values("alpha")
+            plt.plot(sub["alpha"], sub["mean_coi"], marker="s", label=tier)
+    plt.xlabel("contamination alpha")
+    plt.ylabel("mean COI level")
+    plt.title("COI preservation")
+    plt.grid(alpha=0.3)
+    plt.legend()
+    fig2.tight_layout()
+    coi_path = out_dir / f"benchmark_coi_{stamp}.png"
+    fig2.savefig(coi_path, dpi=220)
+    plt.close(fig2)
+
+    focus_alpha = float(df["alpha"].min()) if not df.empty else 0.0
+    alpha_traces = [t for t in traces if abs(float(t["alpha"]) - focus_alpha) < 1e-9]
+    fig3 = plt.figure(figsize=(11, 4.5))
+    for item in alpha_traces:
+        xs = np.arange(len(item["mean_price_trace"]))
+        ys = np.asarray(item["mean_price_trace"], dtype=np.float32)
+        mode = item.get("mode")
+        label = f"{item['tier']}:{mode}" if mode is not None else str(item["tier"])
+        plt.plot(xs, ys, label=label)
+    plt.xlabel("step")
+    plt.ylabel("mean price")
+    plt.title(f"Price evolution (alpha={focus_alpha:.2f})")
+    plt.grid(alpha=0.3)
+    plt.legend()
+    fig3.tight_layout()
+    price_path = out_dir / f"benchmark_price_trace_{stamp}.png"
+    fig3.savefig(price_path, dpi=220)
+    plt.close(fig3)
+
+    return rev_path, coi_path, price_path
+
+
+def _run_with_args(args):
+    compare_robust = _truthy(os.environ.get("PHANTOM_BENCHMARK_COMPARE_ROBUST"))
+    robust_modes = [False, True] if compare_robust else [bool(args.no_robust)]
+
+    base_overrides = {
+        "seed": args.seed,
+        "total_timesteps": args.total_timesteps,
+        "n_products": args.n_products,
+        "N": args.N,
+        "lambda_coi": args.lambda_coi,
+        "robust_radius": args.robust_radius,
+        "robust_points": args.robust_points,
+        "price_low": args.price_low,
+        "price_high": args.price_high,
+        "action_levels": args.action_levels,
+        "action_scale_low": args.action_scale_low,
+        "action_scale_high": args.action_scale_high,
+        "max_steps": args.max_steps,
+        "learning_rate": args.learning_rate,
+        "batch_size": args.batch_size,
+        "n_steps": args.n_steps,
+        "linear_warmup_steps": args.linear_warmup_steps,
+        "device": args.device,
+    }
+    tiers = _parse_list(args.tiers)
+    alpha_values = _parse_float_list(args.alpha_values)
+
+    all_frames: list[pd.DataFrame] = []
+    all_traces: list[dict] = []
+    for no_robust in robust_modes:
+        overrides = dict(base_overrides)
+        overrides["no_robust"] = bool(no_robust)
+        cfg = TrainSpec.from_flat(
+            {k: v for k, v in overrides.items() if v is not None}
+        ).to_flat_dict()
+        cfg["linear_warmup_steps"] = int(args.linear_warmup_steps)
+        df_mode, traces_mode = run_benchmark(cfg, tiers, alpha_values, args.episodes)
+        mode_label = "no_robust" if no_robust else "robust"
+        df_mode["mode"] = mode_label
+        for trace in traces_mode:
+            trace["mode"] = mode_label
+        all_frames.append(df_mode)
+        all_traces.extend(traces_mode)
+
+    df = pd.concat(all_frames, ignore_index=True) if all_frames else pd.DataFrame()
+    traces = all_traces
+
+    out_dir = Path(args.output_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    stamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
+    csv_path = out_dir / f"benchmark_{stamp}.csv"
+    trace_path = out_dir / f"benchmark_traces_{stamp}.json"
+    df.to_csv(csv_path, index=False)
+    trace_path.write_text(json.dumps(traces, indent=2))
+    rev_path, coi_path, price_path = _plot_outputs(df, traces, out_dir, stamp)
+
+    if not df.empty:
+        best_idx = int(df["mean_revenue"].idxmax())
+        best = df.iloc[best_idx]
+        print(
+            "BEST_TIER="
+            + json.dumps(
+                {
+                    "tier": best["tier"],
+                    "mode": best.get("mode", "robust"),
+                    "alpha": float(best["alpha"]),
+                    "mean_revenue": float(best["mean_revenue"]),
+                    "mean_coi": float(best["mean_coi"]),
+                }
+            )
+        )
+    print(f"BENCHMARK_CSV={csv_path}")
+    print(f"BENCHMARK_TRACES={trace_path}")
+    print(f"BENCHMARK_PLOT_REVENUE={rev_path}")
+    print(f"BENCHMARK_PLOT_COI={coi_path}")
+    print(f"BENCHMARK_PLOT_PRICE={price_path}")
+
+
+def run_cli(raw_args: list[str] | None = None):
+    parser = argparse.ArgumentParser(description="PHANTOM benchmark orchestrator")
+    parser.add_argument("--project", default="capstone")
+    parser.add_argument("--tiers", default="static,surge,linear,qtable,ppo")
+    parser.add_argument("--alpha-values", default="0.0,0.3,0.6")
+    parser.add_argument("--episodes", type=int, default=10)
+    parser.add_argument("--output-dir", default="engine/studies/results")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--total-timesteps", type=int, default=25_000)
+    parser.add_argument("--n-products", type=int, default=10)
+    parser.add_argument("--N", type=int, default=100)
+    parser.add_argument("--lambda-coi", type=float, default=0.2)
+    parser.add_argument("--robust-radius", type=float, default=0.15)
+    parser.add_argument("--robust-points", type=int, default=5)
+    parser.add_argument("--price-low", type=float, default=10.0)
+    parser.add_argument("--price-high", type=float, default=150.0)
+    parser.add_argument("--action-levels", type=int, default=9)
+    parser.add_argument("--action-scale-low", type=float, default=0.8)
+    parser.add_argument("--action-scale-high", type=float, default=1.2)
+    parser.add_argument("--max-steps", type=int, default=100)
+    parser.add_argument("--learning-rate", type=float, default=3e-4)
+    parser.add_argument("--batch-size", type=int, default=256)
+    parser.add_argument("--n-steps", type=int, default=2048)
+    parser.add_argument("--linear-warmup-steps", type=int, default=800)
+    parser.add_argument("--device", type=str, default="auto")
+    parser.add_argument("--no-robust", action="store_true")
+    parser.add_argument("--no-wandb", action="store_true")
+    parser.add_argument("--offline", action="store_true")
+    parser.add_argument("--sweep-agent", action="store_true")
+    parser.add_argument("--sweep-id", type=str)
+    parser.add_argument("--count", type=int, default=0)
+    args = parser.parse_args(raw_args)
+
+    if args.sweep_agent:
+        if args.no_wandb or not HAS_WANDB:
+            raise ValueError("sweep agent requires wandb")
+        if not args.sweep_id:
+            raise ValueError("--sweep-id is required with --sweep-agent")
+
+        def _sweep_run():
+            run = wandb.init(mode="offline" if args.offline else "online")
+            try:
+                key_to_attr = {
+                    "tiers": "tiers",
+                    "alpha_values": "alpha_values",
+                    "episodes": "episodes",
+                    "total_timesteps": "total_timesteps",
+                    "lambda_coi": "lambda_coi",
+                    "robust_radius": "robust_radius",
+                    "robust_points": "robust_points",
+                    "learning_rate": "learning_rate",
+                    "batch_size": "batch_size",
+                    "n_steps": "n_steps",
+                    "no_robust": "no_robust",
+                    "device": "device",
+                }
+                for key in (
+                    "tiers",
+                    "alpha_values",
+                    "episodes",
+                    "total_timesteps",
+                    "lambda_coi",
+                    "robust_radius",
+                    "robust_points",
+                    "learning_rate",
+                    "batch_size",
+                    "n_steps",
+                    "no_robust",
+                    "device",
+                ):
+                    if key in wandb.config:
+                        setattr(args, key_to_attr[key], wandb.config[key])
+                _run_with_args(args)
+            finally:
+                if run is not None:
+                    wandb.finish()
+
+        wandb.agent(
+            args.sweep_id,
+            function=_sweep_run,
+            count=args.count if args.count > 0 else None,
+        )
+        return
+
+    if args.no_wandb or not HAS_WANDB:
+        _run_with_args(args)
+        return
+
+    run = wandb.init(
+        project=args.project,
+        name=f"benchmark-{datetime.now(UTC).strftime('%m%d-%H%M%S')}",
+        tags=[
+            "benchmark",
+            "robust-compare"
+            if _truthy(os.environ.get("PHANTOM_BENCHMARK_COMPARE_ROBUST"))
+            else "single-mode",
+        ],
+        config={
+            "run.kind": "benchmark",
+            "tiers": args.tiers,
+            "alpha_values": args.alpha_values,
+            "episodes": args.episodes,
+            "total_timesteps": args.total_timesteps,
+            "lambda_coi": args.lambda_coi,
+            "robust_radius": args.robust_radius,
+            "robust_points": args.robust_points,
+            "learning_rate": args.learning_rate,
+            "device": args.device,
+        },
+        mode="offline" if args.offline else "online",
+    )
+    try:
+        _run_with_args(args)
+    finally:
+        if run is not None:
+            wandb.finish()
+
+
+if __name__ == "__main__":
+    run_cli()

engine/jax/train.py

@@ -624,8 +624,8 @@ def evaluate_policy(
         revenues.append(ep_revenue)
 
     return {
-        "eval/reward": float(np.mean(rewards)),
-        "eval/revenue": float(np.mean(revenues)),
+        "eval/reward_mean": float(np.mean(rewards)),
+        "eval/revenue_mean": float(np.mean(revenues)),
         "eval/reward_std": float(np.std(rewards)),
         "eval/revenue_std": float(np.std(revenues)),
     }
@@ -665,8 +665,8 @@ def _evaluate_q_network(
         revenues.append(ep_revenue)
 
     return {
-        "eval/reward": float(np.mean(rewards)),
-        "eval/revenue": float(np.mean(revenues)),
+        "eval/reward_mean": float(np.mean(rewards)),
+        "eval/revenue_mean": float(np.mean(revenues)),
         "eval/reward_std": float(np.std(rewards)),
         "eval/revenue_std": float(np.std(revenues)),
     }
@@ -713,8 +713,8 @@ def _evaluate_q_table(
         revenues.append(ep_revenue)
 
     return {
-        "eval/reward": float(np.mean(rewards)),
-        "eval/revenue": float(np.mean(revenues)),
+        "eval/reward_mean": float(np.mean(rewards)),
+        "eval/revenue_mean": float(np.mean(revenues)),
         "eval/reward_std": float(np.std(rewards)),
         "eval/revenue_std": float(np.std(revenues)),
     }
@@ -831,8 +831,8 @@ def _train_actor_critic(
         if is_primary and HAS_WANDB and wandb.run is not None:
             wandb.log(
                 {
-                    "train/reward": float(segment_values["reward"].mean()),
-                    "train/revenue": float(segment_values["revenue"].mean()),
+                    "train/reward_mean": float(segment_values["reward"].mean()),
+                    "train/revenue_mean": float(segment_values["revenue"].mean()),
                     "train/agent_prob": float(segment_values["agent_prob"].mean()),
                     "train/alpha_adv": float(segment_values["alpha_adv"].mean()),
                     "train/coi_leakage": float(segment_values["coi_leakage"].mean()),
@@ -873,8 +873,8 @@ def _train_actor_critic(
     train_state = final_runner[0]
     denom = float(metric_count) if metric_count > 0 else 1.0
     metrics = {
-        "train/reward": float(metric_sums["reward"] / denom),
-        "train/revenue": float(metric_sums["revenue"] / denom),
+        "train/reward_mean": float(metric_sums["reward"] / denom),
+        "train/revenue_mean": float(metric_sums["revenue"] / denom),
         "train/agent_prob": float(metric_sums["agent_prob"] / denom),
         "train/alpha_adv": float(metric_sums["alpha_adv"] / denom),
         "train/coi_leakage": float(metric_sums["coi_leakage"] / denom),
@@ -1052,14 +1052,14 @@ def _train_dqn(cfg: dict[str, Any]) -> tuple[dict[str, Any], dict[str, float]]:
         ):
             wandb.log(
                 {
-                    "train/reward": metric_sums["reward"] / max(metric_count, 1),
-                    "train/revenue": metric_sums["revenue"] / max(metric_count, 1),
+                    "train/reward_mean": metric_sums["reward"] / max(metric_count, 1),
+                    "train/revenue_mean": metric_sums["revenue"] / max(metric_count, 1),
                     "train/agent_prob": metric_sums["agent_prob"]
                     / max(metric_count, 1),
                     "train/alpha_adv": metric_sums["alpha_adv"] / max(metric_count, 1),
                     "train/coi_leakage": metric_sums["coi_leakage"]
                     / max(metric_count, 1),
-                    "train/dqn_loss": metric_sums["loss"] / max(loss_count, 1),
+                    "train/loss": metric_sums["loss"] / max(loss_count, 1),
                     "train/epsilon": epsilon_value,
                     "train/global_step": global_step,
                 },
@@ -1090,12 +1090,12 @@ def _train_dqn(cfg: dict[str, Any]) -> tuple[dict[str, Any], dict[str, float]]:
 
     denom = float(metric_count) if metric_count > 0 else 1.0
     metrics = {
-        "train/reward": float(metric_sums["reward"] / denom),
-        "train/revenue": float(metric_sums["revenue"] / denom),
+        "train/reward_mean": float(metric_sums["reward"] / denom),
+        "train/revenue_mean": float(metric_sums["revenue"] / denom),
         "train/agent_prob": float(metric_sums["agent_prob"] / denom),
         "train/alpha_adv": float(metric_sums["alpha_adv"] / denom),
         "train/coi_leakage": float(metric_sums["coi_leakage"] / denom),
-        "train/dqn_loss": float(metric_sums["loss"] / max(loss_count, 1)),
+        "train/loss": float(metric_sums["loss"] / max(loss_count, 1)),
         "train/global_step": total_steps,
     }
 
@@ -1236,8 +1236,8 @@ def _train_qtable(cfg: dict[str, Any]) -> tuple[dict[str, Any], dict[str, float]
         ):
             wandb.log(
                 {
-                    "train/reward": metric_sums["reward"] / max(metric_count, 1),
-                    "train/revenue": metric_sums["revenue"] / max(metric_count, 1),
+                    "train/reward_mean": metric_sums["reward"] / max(metric_count, 1),
+                    "train/revenue_mean": metric_sums["revenue"] / max(metric_count, 1),
                     "train/agent_prob": metric_sums["agent_prob"]
                     / max(metric_count, 1),
                     "train/alpha_adv": metric_sums["alpha_adv"] / max(metric_count, 1),
@@ -1269,8 +1269,8 @@ def _train_qtable(cfg: dict[str, Any]) -> tuple[dict[str, Any], dict[str, float]
 
     denom = float(metric_count) if metric_count > 0 else 1.0
     metrics = {
-        "train/reward": float(metric_sums["reward"] / denom),
-        "train/revenue": float(metric_sums["revenue"] / denom),
+        "train/reward_mean": float(metric_sums["reward"] / denom),
+        "train/revenue_mean": float(metric_sums["revenue"] / denom),
         "train/agent_prob": float(metric_sums["agent_prob"] / denom),
         "train/alpha_adv": float(metric_sums["alpha_adv"] / denom),
         "train/coi_leakage": float(metric_sums["coi_leakage"] / denom),

engine/lib/__init__.py

@@ -1,38 +1,39 @@
-from .demand import estimate_demand, estimate_weighted_demand, generate_demand_for_actor
-from .behavior import sample_behavior, get_transition_models, trajectory_to_events
-from .render import DashboardRenderer, style_axis
-from .wrappers import EconomicMetricsWrapper
-from .callbacks import MetricsCallback, EvalMetricsCallback, CheckpointArtifactCallback
-from .providers import (
-    ProviderBenchmark,
-    ProviderResult,
-    BenchmarkConfig,
-    RandomBaseline,
-    SurgeBaseline,
-)
-from .coi import compute_uplift_coi, extract_purchases, compute_agent_probability
-from .discrete import EventQTable
+from __future__ import annotations
 
-__all__ = [
-    "estimate_demand",
-    "estimate_weighted_demand",
-    "generate_demand_for_actor",
-    "sample_behavior",
-    "get_transition_models",
-    "trajectory_to_events",
-    "DashboardRenderer",
-    "style_axis",
-    "EconomicMetricsWrapper",
-    "MetricsCallback",
-    "EvalMetricsCallback",
-    "CheckpointArtifactCallback",
-    "ProviderBenchmark",
-    "ProviderResult",
-    "BenchmarkConfig",
-    "RandomBaseline",
-    "SurgeBaseline",
-    "compute_uplift_coi",
-    "extract_purchases",
-    "compute_agent_probability",
-    "EventQTable",
-]
+from importlib import import_module
+
+_EXPORTS: dict[str, tuple[str, str]] = {
+    "estimate_demand": (".demand", "estimate_demand"),
+    "estimate_weighted_demand": (".demand", "estimate_weighted_demand"),
+    "generate_demand_for_actor": (".demand", "generate_demand_for_actor"),
+    "sample_behavior": (".behavior", "sample_behavior"),
+    "get_transition_models": (".behavior", "get_transition_models"),
+    "trajectory_to_events": (".behavior", "trajectory_to_events"),
+    "DashboardRenderer": (".render", "DashboardRenderer"),
+    "style_axis": (".render", "style_axis"),
+    "EconomicMetricsWrapper": (".wrappers", "EconomicMetricsWrapper"),
+    "MetricsCallback": (".callbacks", "MetricsCallback"),
+    "EvalMetricsCallback": (".callbacks", "EvalMetricsCallback"),
+    "CheckpointArtifactCallback": (".callbacks", "CheckpointArtifactCallback"),
+    "ProviderBenchmark": (".providers", "ProviderBenchmark"),
+    "ProviderResult": (".providers", "ProviderResult"),
+    "BenchmarkConfig": (".providers", "BenchmarkConfig"),
+    "RandomBaseline": (".providers", "RandomBaseline"),
+    "SurgeBaseline": (".providers", "SurgeBaseline"),
+    "compute_uplift_coi": (".coi", "compute_uplift_coi"),
+    "extract_purchases": (".coi", "extract_purchases"),
+    "compute_agent_probability": (".coi", "compute_agent_probability"),
+    "EventQTable": (".discrete", "EventQTable"),
+}
+
+__all__ = sorted(_EXPORTS)
+
+
+def __getattr__(name: str):
+    if name not in _EXPORTS:
+        raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
+    module_name, attr_name = _EXPORTS[name]
+    module = import_module(module_name, package=__name__)
+    value = getattr(module, attr_name)
+    globals()[name] = value
+    return value

engine/lib/callbacks.py

@@ -38,19 +38,19 @@ class MetricsCallback(BaseCallback):
             t = self.num_timesteps
 
             payload = {
-                "economics/revenue": econ["revenue"],
-                "economics/margin": econ["margin"],
-                "coi/level": econ["coi_level"],
-                "economics/regret": econ["regret"],
+                "train/revenue_step": econ["revenue"],
+                "train/margin_step": econ["margin"],
+                "train/coi_level": econ["coi_level"],
+                "train/regret_step": econ["regret"],
             }
             if "coi_mix" in econ:
-                payload["coi/mix"] = econ["coi_mix"]
+                payload["train/coi_mix"] = econ["coi_mix"]
             if "coi_base" in econ:
-                payload["coi/base"] = econ["coi_base"]
+                payload["train/coi_base"] = econ["coi_base"]
             if "coi_leakage" in econ:
-                payload["coi/leakage"] = econ["coi_leakage"]
+                payload["train/coi_leakage"] = econ["coi_leakage"]
             if "coi_penalty" in econ:
-                payload["coi/penalty"] = econ["coi_penalty"]
+                payload["train/coi_penalty"] = econ["coi_penalty"]
             wandb.log(payload, step=t)
 
             self._episode_revenues.append(econ["revenue"])
@@ -76,8 +76,8 @@ class MetricsCallback(BaseCallback):
             return
         wandb.log(
             {
-                "episode/mean_revenue": np.mean(self._episode_revenues),
-                "episode/total_revenue": np.sum(self._episode_revenues),
+                "train/revenue_rollout_mean": np.mean(self._episode_revenues),
+                "train/revenue_rollout_total": np.sum(self._episode_revenues),
             },
             step=self.num_timesteps,
         )
@@ -164,8 +164,8 @@ class EvalMetricsCallback(EvalCallback):
         if self.n_calls % self.eval_freq == 0 and hasattr(self, "last_mean_reward"):
             wandb.log(
                 {
-                    "eval/mean_reward": self.last_mean_reward,
-                    "eval/mean_revenue": np.mean(self._eval_revenues)
+                    "eval/reward_mean": self.last_mean_reward,
+                    "eval/revenue_mean": np.mean(self._eval_revenues)
                     if self._eval_revenues
                     else 0,
                 },

engine/lib/tiers.py

@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Protocol
+
+import numpy as np
+
+
+class PolicyLike(Protocol):
+    def predict(self, obs: np.ndarray, deterministic: bool = True): ...
+
+
+class StaticPolicy:
+    def __init__(self, n_actions: int):
+        self._action = int(max(0, n_actions // 2))
+
+    def predict(self, obs: np.ndarray, deterministic: bool = True):
+        return self._action, None
+
+
+class SurgePolicy:
+    def __init__(
+        self,
+        n_actions: int,
+        n_products: int,
+        high_threshold: float = 60.0,
+        low_threshold: float = 30.0,
+    ):
+        self.n_actions = int(n_actions)
+        self.n_products = int(n_products)
+        self.mid = self.n_actions // 2
+        self.high_t = float(high_threshold)
+        self.low_t = float(low_threshold)
+
+    def predict(self, obs: np.ndarray, deterministic: bool = True):
+        obs_arr = np.asarray(obs, dtype=np.float32)
+        demand = obs_arr[: self.n_products]
+        demand_mean = float(np.mean(demand)) if demand.size > 0 else 0.0
+        if demand_mean >= self.high_t:
+            return min(self.mid + 2, self.n_actions - 1), None
+        if demand_mean <= self.low_t:
+            return max(self.mid - 2, 0), None
+        return self.mid, None
+
+
+@dataclass
+class LinearElasticityPolicy:
+    n_actions: int
+    n_products: int
+    price_low: float
+    price_high: float
+
+    def __post_init__(self):
+        self.n_actions = int(self.n_actions)
+        self.n_products = int(self.n_products)
+        self.price_low = float(self.price_low)
+        self.price_high = float(self.price_high)
+        self._target_price = 0.5 * (self.price_low + self.price_high)
+        self._action_scales = np.linspace(0.8, 1.2, self.n_actions)
+
+    def fit(self, env, warmup_steps: int = 800, seed: int = 42):
+        rng = np.random.default_rng(int(seed))
+        obs, _ = env.reset(seed=int(seed))
+        prices: list[float] = []
+        demands: list[float] = []
+
+        for _ in range(int(max(10, warmup_steps))):
+            action = int(rng.integers(0, self.n_actions))
+            obs, _, term, trunc, info = env.step(action)
+            done = bool(term or trunc)
+
+            p = np.asarray(info.get("prices", []), dtype=np.float32)
+            d = np.asarray(info.get("demand", []), dtype=np.float32)
+            if p.size > 0 and d.size > 0:
+                prices.append(float(np.mean(p)))
+                demands.append(float(np.mean(d)))
+
+            if done:
+                obs, _ = env.reset()
+
+        if len(prices) < 8:
+            self._target_price = 0.5 * (self.price_low + self.price_high)
+            return self
+
+        slope, intercept = np.polyfit(np.asarray(prices), np.asarray(demands), 1)
+        if slope < -1e-6:
+            p_star = -intercept / (2.0 * slope)
+            self._target_price = float(np.clip(p_star, self.price_low, self.price_high))
+        else:
+            self._target_price = 0.5 * (self.price_low + self.price_high)
+        return self
+
+    def predict(self, obs: np.ndarray, deterministic: bool = True):
+        obs_arr = np.asarray(obs, dtype=np.float32)
+        cur_prices = obs_arr[self.n_products : 2 * self.n_products]
+        cur_mean = (
+            float(np.mean(cur_prices)) if cur_prices.size > 0 else self._target_price
+        )
+        scale = self._target_price / max(cur_mean, 1e-6)
+        action = int(np.argmin(np.abs(self._action_scales - scale)))
+        return int(np.clip(action, 0, self.n_actions - 1)), None

engine/orchestrators/__init__.py

@@ -0,0 +1,5 @@
+from .benchmark import run_benchmark_cli
+from .sweep_agent import run_sweep_agent
+from .train import run_train_once
+
+__all__ = ["run_benchmark_cli", "run_sweep_agent", "run_train_once"]

engine/orchestrators/benchmark.py

@@ -0,0 +1,7 @@
+from __future__ import annotations
+
+
+def run_benchmark_cli(raw_args: list[str] | None = None) -> None:
+    from ..benchmark import run_cli
+
+    run_cli(raw_args)

engine/orchestrators/sweep_agent.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from typing import Any, Mapping, Sequence
+
+from ..spec import TrainSpec, run_name
+from ..telemetry.wandb import (
+    current_config,
+    finish_run,
+    get_wandb_module,
+    init_run,
+    run_agent,
+)
+from .train import run_with_active_sweep_run
+
+
+def run_sweep_agent(
+    *,
+    project: str,
+    sweep_id: str,
+    count: int,
+    offline: bool,
+    no_wandb: bool,
+    base_overrides: Mapping[str, Any],
+    kind: str,
+    scenario: str,
+    group: str | None,
+    extra_tags: Sequence[str],
+) -> None:
+    if no_wandb:
+        raise ValueError("sweep agent requires wandb")
+    if not sweep_id:
+        raise ValueError("--sweep-id is required with --sweep-agent")
+    if get_wandb_module() is None:
+        raise ImportError("wandb is required for sweep runs")
+
+    mode = "offline" if offline else "online"
+
+    def _sweep_trial() -> None:
+        run = init_run(mode=mode, project=project, group=group, sweep_mode=True)
+        try:
+            merged = dict(base_overrides)
+            merged.update(current_config())
+            spec = TrainSpec.from_flat(merged)
+            if run is not None:
+                run.name = run_name(spec, kind=kind, scenario=scenario)
+            run_with_active_sweep_run(
+                spec,
+                kind=kind,
+                scenario=scenario,
+                group=group,
+                extra_tags=extra_tags,
+            )
+        finally:
+            finish_run()
+
+    run_agent(
+        sweep_id,
+        _sweep_trial,
+        count=count if count > 0 else None,
+    )

engine/orchestrators/train.py

@@ -0,0 +1,129 @@
+from __future__ import annotations
+
+import json
+from typing import Any, Sequence
+
+from ..spec import TrainSpec, run_metadata, run_name
+from ..telemetry.wandb import (
+    finish_run,
+    get_wandb_module,
+    init_run,
+    log_metrics,
+    update_run_config,
+    update_summary,
+)
+from ..train_core import run_train
+
+
+def _tags_for_run(spec: TrainSpec, kind: str, extra_tags: Sequence[str]) -> list[str]:
+    tags = [
+        kind,
+        spec.algorithm.name,
+        spec.runtime.backend,
+        "vanilla" if spec.study.no_robust else "robust",
+    ]
+    tags.extend([tag for tag in extra_tags if tag])
+    return tags
+
+
+def _print_local_metrics(metrics: dict[str, Any]) -> None:
+    print(json.dumps(metrics, indent=2))
+    print("PHANTOM_METRICS:" + json.dumps(metrics))
+
+
+def _should_print_local(spec: TrainSpec) -> bool:
+    if not spec.runtime.use_jax:
+        return True
+    try:
+        import jax
+
+        return int(jax.process_index()) == 0
+    except Exception:
+        return True
+
+
+def _is_non_primary_jax_worker(spec: TrainSpec) -> bool:
+    if not spec.runtime.use_jax:
+        return False
+    try:
+        import jax
+
+        return int(jax.process_count()) > 1 and int(jax.process_index()) != 0
+    except Exception:
+        return False
+
+
+def run_train_once(
+    spec: TrainSpec,
+    *,
+    project: str,
+    offline: bool,
+    no_wandb: bool,
+    kind: str,
+    scenario: str,
+    group: str | None,
+    extra_tags: Sequence[str],
+) -> dict[str, Any]:
+    wandb = get_wandb_module()
+    if no_wandb or wandb is None or _is_non_primary_jax_worker(spec):
+        result = run_train(spec)
+        if _should_print_local(spec):
+            _print_local_metrics(result.metrics)
+        return result.metrics
+
+    mode = "offline" if offline else "online"
+    tags = _tags_for_run(spec, kind, extra_tags)
+    metadata = run_metadata(
+        spec,
+        kind=kind,
+        scenario=scenario,
+        group=group,
+        tags=tags,
+    )
+    config = spec.to_flat_dict()
+    config.update(metadata)
+    name = run_name(spec, kind=kind, scenario=scenario)
+    init_run(
+        mode=mode,
+        project=project,
+        config=config,
+        name=name,
+        tags=tags,
+        group=group,
+        sweep_mode=False,
+    )
+
+    try:
+        result = run_train(spec)
+        metrics = result.metrics
+        step = int(metrics.get("train/global_step", spec.runtime.total_timesteps))
+        log_metrics(metrics, step=step)
+        update_summary(metrics)
+        return metrics
+    finally:
+        finish_run()
+
+
+def run_with_active_sweep_run(
+    spec: TrainSpec,
+    *,
+    kind: str,
+    scenario: str,
+    group: str | None,
+    extra_tags: Sequence[str],
+) -> dict[str, Any]:
+    tags = _tags_for_run(spec, kind, extra_tags)
+    metadata = run_metadata(
+        spec,
+        kind=kind,
+        scenario=scenario,
+        group=group,
+        tags=tags,
+    )
+    update_run_config({**spec.to_flat_dict(), **metadata})
+    result = run_train(spec)
+    metrics = result.metrics
+    step = int(metrics.get("train/global_step", spec.runtime.total_timesteps))
+    log_metrics(metrics, step=step)
+    update_summary(metrics)
+    return metrics

engine/project.json

@@ -31,6 +31,26 @@
         "cwd": "."
       }
     },
+    "benchmark": {
+      "executor": "nx:run-commands",
+      "dependsOn": [
+        "install"
+      ],
+      "options": {
+        "command": "bash scripts/nx_research.sh benchmark",
+        "cwd": "."
+      }
+    },
+    "benchmark-agent": {
+      "executor": "nx:run-commands",
+      "dependsOn": [
+        "install"
+      ],
+      "options": {
+        "command": "bash scripts/nx_research.sh benchmark-agent",
+        "cwd": "."
+      }
+    },
     "train-agent": {
       "executor": "nx:run-commands",
       "dependsOn": [

engine/spec.py

@@ -0,0 +1,340 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import os
+from typing import Any, Mapping, Sequence
+
+
+def _truthy(value: str | bool | None) -> bool:
+    if isinstance(value, bool):
+        return value
+    if value is None:
+        return False
+    return str(value).strip().lower() in {"1", "true", "yes", "on"}
+
+
+def _normalize_keys(raw: Mapping[str, Any]) -> dict[str, Any]:
+    alias_map = {
+        "algorithm": "algo",
+        "algorithm.name": "algo",
+        "env.n_products": "n_products",
+        "env.action_levels": "action_levels",
+        "env.action_scale_low": "action_scale_low",
+        "env.action_scale_high": "action_scale_high",
+        "env.price_low": "price_low",
+        "env.price_high": "price_high",
+        "env.max_steps": "max_steps",
+        "env.margin_floor": "margin_floor",
+        "env.margin_floor_patience": "margin_floor_patience",
+        "env.n_sessions": "N",
+        "study.alpha": "alpha",
+        "study.lambda_coi": "lambda_coi",
+        "study.robust_radius": "robust_radius",
+        "study.robust_points": "robust_points",
+        "study.info_value": "info_value",
+        "study.revenue_weight": "revenue_weight",
+        "optimizer.learning_rate": "learning_rate",
+        "optimizer.gamma": "gamma",
+        "optimizer.batch_size": "batch_size",
+        "optimizer.n_steps": "n_steps",
+        "runtime.backend": "backend",
+        "runtime.device": "device",
+        "runtime.seed": "seed",
+        "runtime.total_timesteps": "total_timesteps",
+        "runtime.checkpoint_interval": "checkpoint_interval",
+        "eval.eval_freq": "eval_freq",
+        "eval.eval_episodes": "eval_episodes",
+    }
+    normalized: dict[str, Any] = {}
+    for key, value in raw.items():
+        canonical = alias_map.get(str(key), str(key))
+        normalized[canonical] = value
+    return normalized
+
+
+@dataclass(frozen=True)
+class AlgorithmSpec:
+    name: str = "ppo"
+
+
+@dataclass(frozen=True)
+class EnvSpec:
+    n_products: int = 10
+    n_sessions: int = 100
+    price_low: float = 10.0
+    price_high: float = 150.0
+    action_levels: int = 9
+    action_scale_low: float = 0.8
+    action_scale_high: float = 1.2
+    max_steps: int = 100
+    margin_floor: float = 0.05
+    margin_floor_patience: int = 5
+
+
+@dataclass(frozen=True)
+class StudySpec:
+    alpha: float = 0.3
+    lambda_coi: float = 0.2
+    robust_radius: float = 0.15
+    robust_points: int = 5
+    info_value: float = 1.0
+    revenue_weight: float = 0.01
+    no_robust: bool = False
+
+
+@dataclass(frozen=True)
+class OptimizerSpec:
+    learning_rate: float = 3e-4
+    gamma: float = 0.99
+    buffer_size: int = 50_000
+    batch_size: int = 256
+    tau: float = 0.005
+    train_freq: int = 1
+    learning_starts: int = 1_000
+    target_update_interval: int = 1_000
+    exploration_fraction: float = 0.2
+    exploration_final_eps: float = 0.05
+    n_steps: int = 2_048
+    n_epochs: int = 10
+    gae_lambda: float = 0.95
+    clip_range: float = 0.2
+    ent_coef: float = 0.0
+    q_lr: float = 0.1
+    q_bins: int = 6
+    eps_start: float = 1.0
+    eps_end: float = 0.05
+    eps_decay: float = 0.9995
+    arch: str = "small"
+    activation: str = "relu"
+    jax_num_envs: int = 16
+    jax_num_steps: int = 128
+    jax_num_minibatches: int = 4
+    jax_update_epochs: int = 4
+    jax_anneal_lr: bool = True
+    vf_coef: float = 0.5
+    max_grad_norm: float = 0.5
+
+
+@dataclass(frozen=True)
+class RuntimeSpec:
+    project: str = "capstone"
+    backend: str = "sb3"
+    device: str = "auto"
+    seed: int = 42
+    total_timesteps: int = 50_000
+    checkpoint_interval: int = 200_000
+    model_dir: str = "engine/models"
+    log_freq: int = 100
+    use_jax: bool = False
+
+
+@dataclass(frozen=True)
+class EvalSpec:
+    eval_freq: int = 1_000
+    eval_episodes: int = 5
+    robust_eval_enabled: bool = True
+
+
+@dataclass(frozen=True)
+class TrainSpec:
+    algorithm: AlgorithmSpec = field(default_factory=AlgorithmSpec)
+    env: EnvSpec = field(default_factory=EnvSpec)
+    study: StudySpec = field(default_factory=StudySpec)
+    optimizer: OptimizerSpec = field(default_factory=OptimizerSpec)
+    runtime: RuntimeSpec = field(default_factory=RuntimeSpec)
+    eval: EvalSpec = field(default_factory=EvalSpec)
+
+    def to_flat_dict(self) -> dict[str, Any]:
+        return {
+            "project": self.runtime.project,
+            "algo": self.algorithm.name,
+            "seed": self.runtime.seed,
+            "total_timesteps": self.runtime.total_timesteps,
+            "eval_episodes": self.eval.eval_episodes,
+            "eval_freq": self.eval.eval_freq,
+            "log_freq": self.runtime.log_freq,
+            "model_dir": self.runtime.model_dir,
+            "backend": self.runtime.backend,
+            "device": self.runtime.device,
+            "use_jax": self.runtime.use_jax,
+            "checkpoint_interval": self.runtime.checkpoint_interval,
+            "n_products": self.env.n_products,
+            "N": self.env.n_sessions,
+            "price_low": self.env.price_low,
+            "price_high": self.env.price_high,
+            "action_levels": self.env.action_levels,
+            "action_scale_low": self.env.action_scale_low,
+            "action_scale_high": self.env.action_scale_high,
+            "max_steps": self.env.max_steps,
+            "margin_floor": self.env.margin_floor,
+            "margin_floor_patience": self.env.margin_floor_patience,
+            "alpha": self.study.alpha,
+            "lambda_coi": self.study.lambda_coi,
+            "robust_radius": self.study.robust_radius,
+            "robust_points": self.study.robust_points,
+            "info_value": self.study.info_value,
+            "revenue_weight": self.study.revenue_weight,
+            "no_robust": self.study.no_robust,
+            "learning_rate": self.optimizer.learning_rate,
+            "gamma": self.optimizer.gamma,
+            "buffer_size": self.optimizer.buffer_size,
+            "batch_size": self.optimizer.batch_size,
+            "tau": self.optimizer.tau,
+            "train_freq": self.optimizer.train_freq,
+            "learning_starts": self.optimizer.learning_starts,
+            "target_update_interval": self.optimizer.target_update_interval,
+            "exploration_fraction": self.optimizer.exploration_fraction,
+            "exploration_final_eps": self.optimizer.exploration_final_eps,
+            "n_steps": self.optimizer.n_steps,
+            "n_epochs": self.optimizer.n_epochs,
+            "gae_lambda": self.optimizer.gae_lambda,
+            "clip_range": self.optimizer.clip_range,
+            "ent_coef": self.optimizer.ent_coef,
+            "q_lr": self.optimizer.q_lr,
+            "q_bins": self.optimizer.q_bins,
+            "eps_start": self.optimizer.eps_start,
+            "eps_end": self.optimizer.eps_end,
+            "eps_decay": self.optimizer.eps_decay,
+            "arch": self.optimizer.arch,
+            "activation": self.optimizer.activation,
+            "jax_num_envs": self.optimizer.jax_num_envs,
+            "jax_num_steps": self.optimizer.jax_num_steps,
+            "jax_num_minibatches": self.optimizer.jax_num_minibatches,
+            "jax_update_epochs": self.optimizer.jax_update_epochs,
+            "jax_anneal_lr": self.optimizer.jax_anneal_lr,
+            "vf_coef": self.optimizer.vf_coef,
+            "max_grad_norm": self.optimizer.max_grad_norm,
+            "robust_eval_enabled": self.eval.robust_eval_enabled,
+        }
+
+    @classmethod
+    def from_flat(
+        cls,
+        raw: Mapping[str, Any] | None = None,
+        *,
+        env_vars: Mapping[str, str] | None = None,
+    ) -> "TrainSpec":
+        base = cls().to_flat_dict()
+        incoming = _normalize_keys(raw or {})
+        base.update({k: v for k, v in incoming.items() if v is not None})
+
+        runtime_env = os.environ if env_vars is None else env_vars
+        base["device"] = str(
+            base.get("device", runtime_env.get("PHANTOM_DEVICE", "auto"))
+        )
+
+        requested_jax = _truthy(base.get("use_jax")) or _truthy(
+            runtime_env.get("PHANTOM_USE_JAX")
+        )
+        backend = str(base.get("backend", "jax" if requested_jax else "sb3")).lower()
+        if backend == "auto":
+            backend = "jax" if requested_jax else "sb3"
+        if backend == "jax":
+            requested_jax = True
+
+        no_robust = _truthy(base.get("no_robust"))
+        if no_robust:
+            base["lambda_coi"] = 0.0
+            base["robust_radius"] = 0.0
+            base["robust_points"] = 1
+
+        return cls(
+            algorithm=AlgorithmSpec(name=str(base["algo"]).lower().strip()),
+            env=EnvSpec(
+                n_products=int(base["n_products"]),
+                n_sessions=int(base["N"]),
+                price_low=float(base["price_low"]),
+                price_high=float(base["price_high"]),
+                action_levels=int(base["action_levels"]),
+                action_scale_low=float(base["action_scale_low"]),
+                action_scale_high=float(base["action_scale_high"]),
+                max_steps=int(base["max_steps"]),
+                margin_floor=float(base["margin_floor"]),
+                margin_floor_patience=int(base["margin_floor_patience"]),
+            ),
+            study=StudySpec(
+                alpha=float(base["alpha"]),
+                lambda_coi=float(base["lambda_coi"]),
+                robust_radius=float(base["robust_radius"]),
+                robust_points=int(base["robust_points"]),
+                info_value=float(base["info_value"]),
+                revenue_weight=float(base["revenue_weight"]),
+                no_robust=no_robust,
+            ),
+            optimizer=OptimizerSpec(
+                learning_rate=float(base["learning_rate"]),
+                gamma=float(base["gamma"]),
+                buffer_size=int(base["buffer_size"]),
+                batch_size=int(base["batch_size"]),
+                tau=float(base["tau"]),
+                train_freq=int(base["train_freq"]),
+                learning_starts=int(base["learning_starts"]),
+                target_update_interval=int(base["target_update_interval"]),
+                exploration_fraction=float(base["exploration_fraction"]),
+                exploration_final_eps=float(base["exploration_final_eps"]),
+                n_steps=int(base["n_steps"]),
+                n_epochs=int(base["n_epochs"]),
+                gae_lambda=float(base["gae_lambda"]),
+                clip_range=float(base["clip_range"]),
+                ent_coef=float(base["ent_coef"]),
+                q_lr=float(base["q_lr"]),
+                q_bins=int(base["q_bins"]),
+                eps_start=float(base["eps_start"]),
+                eps_end=float(base["eps_end"]),
+                eps_decay=float(base["eps_decay"]),
+                arch=str(base["arch"]),
+                activation=str(base["activation"]),
+                jax_num_envs=int(base["jax_num_envs"]),
+                jax_num_steps=int(base["jax_num_steps"]),
+                jax_num_minibatches=int(base["jax_num_minibatches"]),
+                jax_update_epochs=int(base["jax_update_epochs"]),
+                jax_anneal_lr=_truthy(base.get("jax_anneal_lr")),
+                vf_coef=float(base["vf_coef"]),
+                max_grad_norm=float(base["max_grad_norm"]),
+            ),
+            runtime=RuntimeSpec(
+                project=str(base["project"]),
+                backend=backend,
+                device=str(base["device"]),
+                seed=int(base["seed"]),
+                total_timesteps=int(base["total_timesteps"]),
+                checkpoint_interval=int(base["checkpoint_interval"]),
+                model_dir=str(base["model_dir"]),
+                log_freq=int(base["log_freq"]),
+                use_jax=requested_jax,
+            ),
+            eval=EvalSpec(
+                eval_freq=int(base["eval_freq"]),
+                eval_episodes=int(base["eval_episodes"]),
+                robust_eval_enabled=_truthy(base.get("robust_eval_enabled", True)),
+            ),
+        )
+
+
+def run_name(spec: TrainSpec, *, kind: str, scenario: str) -> str:
+    return (
+        f"{kind}/{spec.algorithm.name}/{spec.runtime.backend}/"
+        f"{spec.runtime.device}/{scenario}/s{spec.runtime.seed}"
+    )
+
+
+def run_metadata(
+    spec: TrainSpec,
+    *,
+    kind: str,
+    scenario: str,
+    group: str | None = None,
+    tags: Sequence[str] = (),
+) -> dict[str, Any]:
+    metadata: dict[str, Any] = {
+        "run.kind": str(kind),
+        "run.algo": spec.algorithm.name,
+        "run.backend": spec.runtime.backend,
+        "run.device": spec.runtime.device,
+        "run.scenario": str(scenario),
+        "run.seed": spec.runtime.seed,
+        "run.tags": list(tags),
+    }
+    if group:
+        metadata["run.group"] = group
+    return metadata

engine/studies/local_comparison.py

@@ -0,0 +1,136 @@
+import sys
+import numpy as np
+import pandas as pd
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+from gymnasium.wrappers import FlattenObservation
+from stable_baselines3 import PPO
+
+# Add parent directory to path to allow importing engine
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+from engine.wrapper import PHANTOM
+from engine.lib.wrappers import EconomicMetricsWrapper
+from engine.lib.providers import (
+    ProviderBenchmark,
+    BenchmarkConfig,
+    RandomBaseline,
+    SurgeBaseline,
+)
+
+
+def env_factory(alpha: float):
+    """Creates a wrapped PHANTOM environment for testing at a specific alpha level."""
+    # Action levels=9 matches the trained PPO model
+    # n_products=8 matches the pretrained model's expectation of Box(16,)
+    env = PHANTOM(
+        n_products=8,
+        alpha=alpha,
+        N=100,
+        action_levels=9,
+        action_scale_low=0.8,
+        action_scale_high=1.2,
+        max_steps=20,  # Short episodes so simulation goes fast
+        robust_points=1,  # disable expensive adversarial lookaheads
+        render_mode=None,
+    )
+    env = EconomicMetricsWrapper(env)
+    return FlattenObservation(env)
+
+
+def main():
+    print("Loading pre-trained Robust RL model...")
+    model_path = Path(__file__).parent.parent / "models" / "phantom_ppo.zip"
+    if not model_path.exists():
+        print(f"Error: Model not found at {model_path}")
+        print("Please ensure you have a trained model before running this script.")
+        return
+
+    rl_model = PPO.load(model_path)
+
+    # The action space is Discrete(9). Index 4 is the middle (1.0 scale).
+    n_actions = 9
+    mid_action = n_actions // 2
+
+    providers = {
+        "Static (Base)": lambda obs: mid_action,
+        "Random": RandomBaseline(n_actions),
+        "Heuristic Surge": SurgeBaseline(
+            n_actions, high_threshold=60.0, low_threshold=30.0
+        ),
+        "Robust RL (PPO)": lambda obs: rl_model.predict(obs, deterministic=True)[0],
+    }
+
+    config = BenchmarkConfig(
+        n_episodes=10,  # Lower episodes to run faster
+        alpha_range=[0.0, 0.5, 1.0],  # Fewer alpha levels
+        baseline_name="Static (Base)",
+    )
+
+    print(f"\nStarting benchmark across alpha levels: {config.alpha_range}")
+    print(
+        f"Testing {len(providers)} strategies for {config.n_episodes} episodes each...\n"
+    )
+
+    benchmark = ProviderBenchmark(env_factory, providers, config)
+    results = benchmark.run()
+
+    # 1. Print tabular results
+    df = benchmark.to_dataframe()
+    summary = benchmark.summary_table()
+    print("\n--- Benchmark Summary Table ---")
+    print(summary)
+
+    # 2. Save results to CSV for thesis inclusion
+    out_dir = Path(__file__).parent / "results"
+    out_dir.mkdir(exist_ok=True)
+    csv_path = out_dir / "provider_comparison.csv"
+    df.to_csv(csv_path, index=False)
+    print(f"\nSaved raw results to {csv_path}")
+
+    # 3. Plot the degradation of COI / Revenue as alpha increases
+    plt.figure(figsize=(12, 5))
+
+    # Plot 1: Revenue vs Alpha
+    plt.subplot(1, 2, 1)
+    for name in providers.keys():
+        provider_data = df[df["name"] == name]
+        plt.plot(
+            provider_data["alpha"],
+            provider_data["mean_revenue"],
+            marker="o",
+            label=name,
+            linewidth=2,
+        )
+    plt.title("Revenue under Agent Contamination")
+    plt.xlabel("Contamination Level (α)")
+    plt.ylabel("Mean Episode Revenue ($)")
+    plt.grid(True, linestyle="--", alpha=0.7)
+    plt.legend()
+
+    # Plot 2: COI Preservation vs Alpha
+    plt.subplot(1, 2, 2)
+    for name in providers.keys():
+        provider_data = df[df["name"] == name]
+        plt.plot(
+            provider_data["alpha"],
+            provider_data["coi_preserved_pct"],
+            marker="s",
+            label=name,
+            linewidth=2,
+        )
+    plt.title("Cost of Information (COI) Preservation")
+    plt.xlabel("Contamination Level (α)")
+    plt.ylabel("COI Preserved (%)")
+    plt.grid(True, linestyle="--", alpha=0.7)
+    plt.legend()
+
+    plt.tight_layout()
+    plot_path = out_dir / "alpha_degradation_plot.png"
+    plt.savefig(plot_path, dpi=300)
+    print(f"Saved visualization to {plot_path}")
+
+
+if __name__ == "__main__":
+    main()

engine/sweeps/model_mix.yaml

@@ -1,6 +1,6 @@
 method: random
 metric:
-  name: sweep/score
+  name: objective/score
   goal: maximize
 command:
   - ${env}

engine/sweeps/models_only.yaml

@@ -1,6 +1,6 @@
 method: grid
 metric:
-  name: sweep/score
+  name: objective/score
   goal: maximize
 run_cap: 4
 command:

engine/sweeps/sac_tune.yaml

@@ -1,6 +1,6 @@
 method: bayes
 metric:
-  name: sweep/score
+  name: objective/score
   goal: maximize
 command:
   - ${env}

engine/sweeps/small_arch_compare.yaml

@@ -1,6 +1,6 @@
 method: random
 metric:
-  name: sweep/score
+  name: objective/score
   goal: maximize
 command:
   - ${env}

engine/sweeps/tpu_jax.yaml

@@ -1,6 +1,6 @@
 method: bayes
 metric:
-  name: sweep/score
+  name: objective/score
   goal: maximize
 command:
   - ${env}

engine/sweeps/tpu_pod.yaml

@@ -1,6 +1,6 @@
 method: bayes
 metric:
-  name: sweep/score
+  name: objective/score
   goal: maximize
 command:
   - ${env}

engine/telemetry/__init__.py

@@ -0,0 +1,23 @@
+from .metrics import canonicalize_metrics
+from .wandb import (
+    current_config,
+    finish_run,
+    get_wandb_module,
+    init_run,
+    log_metrics,
+    run_agent,
+    update_run_config,
+    update_summary,
+)
+
+__all__ = [
+    "canonicalize_metrics",
+    "current_config",
+    "finish_run",
+    "get_wandb_module",
+    "init_run",
+    "log_metrics",
+    "run_agent",
+    "update_run_config",
+    "update_summary",
+]

engine/telemetry/metrics.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+from typing import Any, Mapping
+
+from ..spec import TrainSpec
+
+
+_ALIASES = {
+    "train/reward": "train/reward_mean",
+    "train/revenue": "train/revenue_mean",
+    "train/dqn_loss": "train/loss",
+    "eval/reward": "eval/reward_mean",
+    "eval/revenue": "eval/revenue_mean",
+    "train/steps_per_second": "runtime/steps_per_second",
+}
+
+
+def _as_float(value: Any, default: float | None = None) -> float | None:
+    if value is None:
+        return default
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def canonicalize_metrics(raw: Mapping[str, Any], spec: TrainSpec) -> dict[str, Any]:
+    metrics: dict[str, Any] = {}
+    for key, value in raw.items():
+        canonical = _ALIASES.get(str(key), str(key))
+        if canonical in metrics and canonical != key:
+            continue
+        metrics[canonical] = value
+
+    metrics.setdefault("train/global_step", spec.runtime.total_timesteps)
+
+    eval_reward = _as_float(metrics.get("eval/reward_mean"), 0.0) or 0.0
+    eval_revenue = _as_float(metrics.get("eval/revenue_mean"), 0.0) or 0.0
+    metrics["objective/score"] = eval_reward + spec.study.revenue_weight * eval_revenue
+
+    margin_mean = _as_float(metrics.get("eval/margin_mean"), None)
+    if margin_mean is not None:
+        metrics["objective/constraint_margin"] = margin_mean - spec.env.margin_floor
+
+    coi_level = _as_float(metrics.get("eval/coi_level_mean"), None)
+    metrics["objective/coi_preserved"] = 0.0 if coi_level is None else coi_level
+
+    metrics["study/alpha"] = spec.study.alpha
+    metrics["study/lambda_coi"] = spec.study.lambda_coi
+    metrics["study/robust_radius"] = spec.study.robust_radius
+    metrics["study/info_value"] = spec.study.info_value
+
+    metrics["runtime/backend"] = spec.runtime.backend
+    metrics["runtime/device"] = spec.runtime.device
+    metrics["runtime/seed"] = spec.runtime.seed
+
+    return metrics

engine/telemetry/wandb.py

@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Iterable, Mapping
+
+
+def get_wandb_module():
+    try:
+        import wandb
+
+        return wandb
+    except ImportError:
+        return None
+
+
+def _require_wandb():
+    wandb = get_wandb_module()
+    if wandb is None:
+        raise ImportError("wandb is required for this workflow")
+    return wandb
+
+
+def init_run(
+    *,
+    mode: str,
+    project: str | None = None,
+    config: Mapping[str, Any] | None = None,
+    name: str | None = None,
+    tags: Iterable[str] | None = None,
+    group: str | None = None,
+    sweep_mode: bool = False,
+):
+    wandb = _require_wandb()
+    kwargs: dict[str, Any] = {"mode": mode}
+    if group:
+        kwargs["group"] = group
+    if sweep_mode:
+        run = wandb.init(**kwargs)
+        if name and run is not None:
+            run.name = name
+        return run
+
+    init_kwargs = dict(kwargs)
+    init_kwargs["project"] = project
+    if config is not None:
+        init_kwargs["config"] = dict(config)
+    if name:
+        init_kwargs["name"] = name
+    if tags:
+        init_kwargs["tags"] = list(tags)
+    return wandb.init(**init_kwargs)
+
+
+def finish_run() -> None:
+    wandb = get_wandb_module()
+    if wandb is not None and wandb.run is not None:
+        wandb.finish()
+
+
+def current_config() -> dict[str, Any]:
+    wandb = get_wandb_module()
+    if wandb is None or wandb.run is None:
+        return {}
+    return {key: wandb.config[key] for key in wandb.config.keys()}
+
+
+def update_run_config(config: Mapping[str, Any]) -> None:
+    wandb = get_wandb_module()
+    if wandb is None or wandb.run is None:
+        return
+    try:
+        wandb.config.update(dict(config), allow_val_change=True)
+    except TypeError:
+        wandb.config.update(dict(config))
+
+
+def log_metrics(metrics: Mapping[str, Any], *, step: int) -> None:
+    wandb = get_wandb_module()
+    if wandb is None or wandb.run is None:
+        return
+    wandb.log(dict(metrics), step=step)
+
+
+def update_summary(metrics: Mapping[str, Any]) -> None:
+    wandb = get_wandb_module()
+    if wandb is None or wandb.run is None:
+        return
+    for key, value in metrics.items():
+        wandb.run.summary[key] = value
+
+
+def run_agent(
+    sweep_id: str,
+    fn: Callable[[], None],
+    *,
+    count: int | None = None,
+) -> None:
+    wandb = _require_wandb()
+    wandb.agent(sweep_id, function=fn, count=count)

engine/train.py

@@ -1,98 +1,10 @@
 from __future__ import annotations
 
 import argparse
-import json
-import os
-from pathlib import Path
-from typing import TYPE_CHECKING
-import numpy as np
+from typing import Any
 
-if TYPE_CHECKING:
-    from .lib.discrete import EventQTable
-
-from .wandb_checkpoint import checkpoint_artifact_name, download_latest_checkpoint
-
-try:
-    import wandb as _wandb
-
-    if hasattr(_wandb, "init") and callable(_wandb.init):
-        wandb = _wandb
-        HAS_WANDB = True
-    else:
-        wandb = None
-        HAS_WANDB = False
-except ImportError:
-    wandb = None
-    HAS_WANDB = False
-
-try:
-    from stable_baselines3 import PPO, A2C, DQN
-    from stable_baselines3.common.callbacks import EvalCallback
-    from stable_baselines3.common.monitor import Monitor
-
-    HAS_SB3 = True
-except ImportError:
-    HAS_SB3 = False
-
-from .jax import JAX_AVAILABLE
-
-
-DEFAULT_CFG = {
-    "project": "phantom-pricing",
-    "algo": "ppo",
-    "seed": 42,
-    "total_timesteps": 50_000,
-    "eval_episodes": 5,
-    "eval_freq": 1_000,
-    "log_freq": 100,
-    "revenue_weight": 0.01,
-    "n_products": 10,
-    "N": 100,
-    "alpha": 0.3,
-    "lambda_coi": 0.2,
-    "robust_radius": 0.15,
-    "robust_points": 5,
-    "no_robust": False,
-    "info_value": 1.0,
-    "price_low": 10.0,
-    "price_high": 150.0,
-    "action_levels": 9,
-    "action_scale_low": 0.8,
-    "action_scale_high": 1.2,
-    "learning_rate": 3e-4,
-    "gamma": 0.99,
-    "buffer_size": 50_000,
-    "batch_size": 256,
-    "tau": 0.005,
-    "train_freq": 1,
-    "learning_starts": 1_000,
-    "target_update_interval": 1_000,
-    "exploration_fraction": 0.2,
-    "exploration_final_eps": 0.05,
-    "n_steps": 2_048,
-    "n_epochs": 10,
-    "gae_lambda": 0.95,
-    "clip_range": 0.2,
-    "ent_coef": 0.0,
-    "q_lr": 0.1,
-    "eps_start": 1.0,
-    "eps_end": 0.05,
-    "eps_decay": 0.9995,
-    "model_dir": "engine/models",
-    "arch": "small",
-    "activation": "relu",
-    "q_bins": 6,
-    "max_steps": 100,
-    "margin_floor": 0.05,
-    "margin_floor_patience": 5,
-    "use_jax": False,
-    "jax_num_envs": 16,
-    "jax_num_steps": 128,
-    "jax_num_minibatches": 4,
-    "jax_update_epochs": 4,
-    "jax_anneal_lr": True,
-    "checkpoint_interval": 200_000,
-}
+from .orchestrators import run_benchmark_cli, run_sweep_agent, run_train_once
+from .spec import TrainSpec
 
 
 def _truthy(value: str | bool | None) -> bool:
@@ -103,423 +15,133 @@ def _truthy(value: str | bool | None) -> bool:
     return str(value).strip().lower() in {"1", "true", "yes", "on"}
 
 
-def _cfg(raw: dict | None = None) -> dict:
-    cfg = dict(DEFAULT_CFG)
-    if raw:
-        cfg.update({k: v for k, v in raw.items() if v is not None})
-    cfg["algo"] = str(cfg["algo"]).lower()
-    cfg["use_jax"] = _truthy(cfg.get("use_jax")) or _truthy(
-        os.environ.get("PHANTOM_USE_JAX")
+def _parse_tags(raw: str | None) -> list[str]:
+    if raw is None:
+        return []
+    return [piece.strip() for piece in str(raw).split(",") if piece.strip()]
+
+
+def _probe_run_kind(argv: list[str]) -> str:
+    probe = argparse.ArgumentParser(add_help=False)
+    probe.add_argument("--run-kind", choices=["train", "benchmark"])
+    probe.add_argument("--run-mode", choices=["train", "benchmark"])
+    args, _ = probe.parse_known_args(argv)
+    return str(args.run_kind or args.run_mode or "train")
+
+
+def _strip_run_kind(argv: list[str]) -> list[str]:
+    stripped: list[str] = []
+    skip_next = False
+    for item in argv:
+        if skip_next:
+            skip_next = False
+            continue
+        if item in {"--run-kind", "--run-mode"}:
+            skip_next = True
+            continue
+        if item.startswith("--run-kind=") or item.startswith("--run-mode="):
+            continue
+        stripped.append(item)
+    return stripped
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="PHANTOM unified training entrypoint")
+    parser.add_argument("--run-kind", choices=["train", "benchmark"], default="train")
+    parser.add_argument("--run-mode", choices=["train", "benchmark"])
+
+    parser.add_argument("--project", default="capstone")
+    parser.add_argument("--scenario", default="default")
+    parser.add_argument("--group", type=str)
+    parser.add_argument("--tags", type=str)
+
+    parser.add_argument("--backend", choices=["auto", "sb3", "jax"], default="auto")
+    parser.add_argument("--algo", choices=["ppo", "a2c", "dqn", "qtable", "sac"])
+    parser.add_argument("--seed", type=int)
+    parser.add_argument("--total-timesteps", type=int)
+    parser.add_argument("--model-dir", type=str)
+    parser.add_argument("--log-freq", type=int)
+    parser.add_argument("--checkpoint-interval", type=int)
+    parser.add_argument("--device", type=str)
+
+    parser.add_argument("--alpha", type=float)
+    parser.add_argument("--N", type=int)
+    parser.add_argument("--n-products", type=int)
+    parser.add_argument("--lambda-coi", type=float)
+    parser.add_argument("--info-value", type=float)
+    parser.add_argument("--robust-radius", type=float)
+    parser.add_argument("--robust-points", type=int)
+    parser.add_argument("--no-robust", action="store_true")
+    parser.add_argument("--revenue-weight", type=float)
+
+    parser.add_argument("--price-low", type=float)
+    parser.add_argument("--price-high", type=float)
+    parser.add_argument("--action-levels", type=int)
+    parser.add_argument("--action-scale-low", type=float)
+    parser.add_argument("--action-scale-high", type=float)
+    parser.add_argument("--max-steps", type=int)
+    parser.add_argument("--margin-floor", type=float)
+    parser.add_argument("--margin-floor-patience", type=int)
+
+    parser.add_argument("--learning-rate", type=float)
+    parser.add_argument("--gamma", type=float)
+    parser.add_argument("--buffer-size", type=int)
+    parser.add_argument("--batch-size", type=int)
+    parser.add_argument("--tau", type=float)
+    parser.add_argument("--train-freq", type=int)
+    parser.add_argument("--learning-starts", type=int)
+    parser.add_argument("--target-update-interval", type=int)
+    parser.add_argument("--exploration-fraction", type=float)
+    parser.add_argument("--exploration-final-eps", type=float)
+    parser.add_argument("--n-steps", type=int)
+    parser.add_argument("--n-epochs", type=int)
+    parser.add_argument("--gae-lambda", type=float)
+    parser.add_argument("--clip-range", type=float)
+    parser.add_argument("--ent-coef", type=float)
+    parser.add_argument("--q-lr", type=float)
+    parser.add_argument("--q-bins", type=int)
+    parser.add_argument("--eps-start", type=float)
+    parser.add_argument("--eps-end", type=float)
+    parser.add_argument("--eps-decay", type=float)
+    parser.add_argument("--arch", type=str)
+    parser.add_argument("--activation", type=str)
+    parser.add_argument("--vf-coef", type=float)
+    parser.add_argument("--max-grad-norm", type=float)
+
+    parser.add_argument("--eval-freq", type=int)
+    parser.add_argument("--eval-episodes", type=int)
+
+    parser.add_argument("--jax", action="store_true")
+    parser.add_argument("--jax-num-envs", type=int)
+    parser.add_argument("--jax-num-steps", type=int)
+    parser.add_argument("--jax-num-minibatches", type=int)
+    parser.add_argument("--jax-update-epochs", type=int)
+    parser.add_argument("--jax-anneal-lr", type=str)
+
+    parser.add_argument("--sweep-agent", action="store_true")
+    parser.add_argument("--sweep-id", type=str)
+    parser.add_argument("--count", type=int, default=0)
+    parser.add_argument("--offline", action="store_true")
+    parser.add_argument("--no-wandb", action="store_true")
+    return parser
+
+
+def _overrides_from_args(args: argparse.Namespace) -> dict[str, Any]:
+    jax_anneal_lr = (
+        _truthy(args.jax_anneal_lr) if args.jax_anneal_lr is not None else None
     )
-    cfg["no_robust"] = _truthy(cfg.get("no_robust"))
-    if cfg["no_robust"]:
-        cfg["lambda_coi"] = 0.0
-        cfg["robust_radius"] = 0.0
-        cfg["robust_points"] = 1
-    return cfg
-
-
-def _wandb_cfg_dict() -> dict:
-    return (
-        {k: wandb.config[k] for k in wandb.config.keys()}
-        if HAS_WANDB and wandb.run
-        else {}
-    )
-
-
-def make_env(cfg: dict):
-    from gymnasium.wrappers import FlattenObservation
-
-    from .wrapper import PHANTOM
-    from .lib.wrappers import EconomicMetricsWrapper
-
-    env = PHANTOM(
-        n_products=int(cfg["n_products"]),
-        alpha=float(cfg["alpha"]),
-        N=int(cfg["N"]),
-        price_bounds=(float(cfg["price_low"]), float(cfg["price_high"])),
-        lambda_coi=float(cfg["lambda_coi"]),
-        robust_radius=float(cfg["robust_radius"]),
-        robust_points=int(cfg["robust_points"]),
-        info_value=float(cfg["info_value"]),
-        action_levels=int(cfg["action_levels"]),
-        action_scale_low=float(cfg["action_scale_low"]),
-        action_scale_high=float(cfg["action_scale_high"]),
-        max_steps=int(cfg.get("max_steps", 100)),
-        margin_floor=float(cfg.get("margin_floor", 0.05)),
-        margin_floor_patience=int(cfg.get("margin_floor_patience", 5)),
-        render_mode=None,
-    )
-    env = EconomicMetricsWrapper(env)
-    env = FlattenObservation(env)
-    return env
-
-
-def _net_arch(name) -> list[int]:
-    presets = {
-        "tiny": [32, 32],
-        "small": [64, 64],
-        "medium": [128, 128],
-        "large": [256, 256],
-    }
-    if isinstance(name, (list, tuple)):
-        return [int(v) for v in name]
-    s = str(name).lower().strip()
-    if s in presets:
-        return presets[s]
-    if "x" in s:
-        try:
-            vals = [int(v) for v in s.split("x") if v]
-            return vals if vals else presets["small"]
-        except ValueError:
-            return presets["small"]
-    return presets["small"]
-
-
-def _activation(name):
-    try:
-        import torch.nn as nn
-    except ImportError:
-        return None
-    return {
-        "relu": nn.ReLU,
-        "tanh": nn.Tanh,
-        "elu": nn.ELU,
-        "leaky_relu": nn.LeakyReLU,
-    }.get(str(name).lower().strip(), nn.ReLU)
-
-
-def _policy_kwargs(cfg: dict) -> dict:
-    kw = {"net_arch": _net_arch(cfg.get("arch", "small"))}
-    act = _activation(cfg.get("activation", "relu"))
-    if act is not None:
-        kw["activation_fn"] = act
-    return kw
-
-
-def _action(agent, obs, deterministic: bool = True):
-    out = agent.predict(obs, deterministic=deterministic)
-    a = out[0] if isinstance(out, tuple) else out
-    if isinstance(a, np.ndarray) and a.size == 1:
-        return int(a.reshape(-1)[0])
-    return a
-
-
-def evaluate(agent, env, episodes: int) -> dict:
-    rewards, revenues = [], []
-    for _ in range(int(episodes)):
-        obs, _ = env.reset()
-        done, ep_r, ep_rev = False, 0.0, 0.0
-        while not done:
-            obs, reward, term, trunc, info = env.step(_action(agent, obs, True))
-            done = term or trunc
-            ep_r += float(reward)
-            ep_rev += float(
-                info.get("economics", {}).get("revenue", info.get("revenue", 0.0))
-            )
-        rewards.append(ep_r)
-        revenues.append(ep_rev)
-    return {
-        "eval/reward": float(np.mean(rewards)),
-        "eval/revenue": float(np.mean(revenues)),
-        "eval/reward_std": float(np.std(rewards)),
-        "eval/revenue_std": float(np.std(revenues)),
-    }
-
-
-def build_model(cfg: dict, env):
-    algo = cfg["algo"]
-    policy_kwargs = _policy_kwargs(cfg)
-    if algo == "sac":
-        raise ValueError("sac is not supported with the discrete core env")
-    if algo == "ppo":
-        return PPO(
-            "MlpPolicy",
-            env,
-            verbose=1,
-            policy_kwargs=policy_kwargs,
-            seed=int(cfg["seed"]),
-            learning_rate=float(cfg["learning_rate"]),
-            n_steps=int(cfg["n_steps"]),
-            batch_size=int(cfg["batch_size"]),
-            n_epochs=int(cfg["n_epochs"]),
-            gamma=float(cfg["gamma"]),
-            gae_lambda=float(cfg["gae_lambda"]),
-            clip_range=float(cfg["clip_range"]),
-            ent_coef=float(cfg["ent_coef"]),
-        )
-    if algo == "a2c":
-        return A2C(
-            "MlpPolicy",
-            env,
-            verbose=1,
-            policy_kwargs=policy_kwargs,
-            seed=int(cfg["seed"]),
-            learning_rate=float(cfg["learning_rate"]),
-            n_steps=max(5, int(cfg["n_steps"]) // 32),
-            gamma=float(cfg["gamma"]),
-            gae_lambda=float(cfg["gae_lambda"]),
-            ent_coef=float(cfg["ent_coef"]),
-        )
-    if algo == "dqn":
-        return DQN(
-            "MlpPolicy",
-            env,
-            verbose=1,
-            policy_kwargs=policy_kwargs,
-            seed=int(cfg["seed"]),
-            learning_rate=float(cfg["learning_rate"]),
-            buffer_size=int(cfg["buffer_size"]),
-            batch_size=int(cfg["batch_size"]),
-            gamma=float(cfg["gamma"]),
-            train_freq=int(cfg["train_freq"]),
-            learning_starts=int(cfg["learning_starts"]),
-            target_update_interval=int(cfg["target_update_interval"]),
-            exploration_fraction=float(cfg["exploration_fraction"]),
-            exploration_final_eps=float(cfg["exploration_final_eps"]),
-        )
-    raise ValueError(f"unsupported algo '{algo}'")
-
-
-def _sb3_model_cls(algo: str):
-    if algo == "ppo":
-        return PPO
-    if algo == "a2c":
-        return A2C
-    if algo == "dqn":
-        return DQN
-    raise ValueError(f"unsupported algo '{algo}'")
-
-
-def train_qtable(cfg: dict) -> tuple["EventQTable", dict]:
-    from .lib.discrete import EventQTable
-
-    np.random.seed(int(cfg["seed"]))
-    env = make_env(cfg)
-    eval_env = make_env(cfg)
-    agent = EventQTable(
-        env.action_space.n,
-        int(cfg["n_products"]),
-        (float(cfg["price_low"]), float(cfg["price_high"])),
-        lr=float(cfg["q_lr"]),
-        gamma=float(cfg["gamma"]),
-        n_bins=int(cfg["q_bins"]),
-    )
-    eps = float(cfg["eps_start"])
-    obs, _ = env.reset(seed=int(cfg["seed"]))
-    for t in range(int(cfg["total_timesteps"])):
-        a, s = agent.act(obs, eps)
-        nxt, reward, term, trunc, info = env.step(a)
-        done = term or trunc
-        agent.update(s, a, float(reward), agent.encode(nxt), done)
-        eps = max(float(cfg["eps_end"]), eps * float(cfg["eps_decay"]))
-        if HAS_WANDB and wandb.run and (t + 1) % int(cfg["log_freq"]) == 0:
-            econ = info.get("economics", {})
-            wandb.log(
-                {
-                    "train/reward": float(reward),
-                    "train/revenue": float(econ.get("revenue", 0.0)),
-                    "train/epsilon": float(eps),
-                },
-                step=t + 1,
-            )
-        obs = env.reset()[0] if done else nxt
-    metrics = evaluate(agent, eval_env, int(cfg["eval_episodes"]))
-    metrics["train/global_step"] = int(cfg["total_timesteps"])
-    env.close()
-    eval_env.close()
-    return agent, metrics
-
-
-def train_sb3(cfg: dict) -> tuple[object, dict]:
-    if not HAS_SB3:
-        raise ImportError("stable-baselines3 is required for SB3 models")
-    from .lib.callbacks import CheckpointArtifactCallback, MetricsCallback
-
-    env = make_env(cfg)
-    eval_env = make_env(cfg)
-    env = Monitor(env)
-    eval_env = Monitor(eval_env)
-    model = build_model(cfg, env)
-    resume_step = 0
-    if HAS_WANDB and wandb.run is not None:
-        sweep_id = getattr(wandb.run, "sweep_id", None)
-        artifact_name = checkpoint_artifact_name(cfg, backend="sb3", sweep_id=sweep_id)
-        checkpoint_file = f"phantom_{cfg['algo']}_checkpoint.zip"
-        restored = download_latest_checkpoint(artifact_name, file_name=checkpoint_file)
-        if restored is not None:
-            checkpoint_path, metadata = restored
-            model = _sb3_model_cls(cfg["algo"]).load(
-                checkpoint_path.as_posix(), env=env
-            )
-            resume_step = int(metadata.get("step", getattr(model, "num_timesteps", 0)))
-            model.num_timesteps = max(
-                int(getattr(model, "num_timesteps", 0)), resume_step
-            )
-
-    cbs = [MetricsCallback(log_histograms=True, log_freq=int(cfg["log_freq"]))]
-    cbs.append(
-        CheckpointArtifactCallback(
-            cfg,
-            interval=int(cfg.get("checkpoint_interval", 10_000)),
-        )
-    )
-    cbs.append(
-        EvalCallback(
-            eval_env,
-            eval_freq=int(cfg["eval_freq"]),
-            n_eval_episodes=int(cfg["eval_episodes"]),
-            deterministic=True,
-            verbose=0,
-        )
-    )
-    target_steps = int(cfg["total_timesteps"])
-    remaining_steps = max(0, target_steps - int(getattr(model, "num_timesteps", 0)))
-    if remaining_steps > 0:
-        model.learn(
-            total_timesteps=remaining_steps,
-            callback=cbs,
-            reset_num_timesteps=False,
-        )
-
-    model_path = Path(cfg["model_dir"])
-    model_path.mkdir(parents=True, exist_ok=True)
-    model.save(str(model_path / f"phantom_{cfg['algo']}"))
-    metrics = evaluate(model, eval_env, int(cfg["eval_episodes"]))
-    metrics["train/global_step"] = int(model.num_timesteps)
-    env.close()
-    eval_env.close()
-    return model, metrics
-
-
-def train_once(cfg: dict) -> dict:
-    algo = cfg["algo"]
-    if cfg.get("use_jax"):
-        if not JAX_AVAILABLE:
-            raise ImportError(
-                "JAX backend requested but JAX is not installed. "
-                "Install engine/jax/requirements.txt and jax[tpu] for TPU runs."
-            )
-        try:
-            from .jax.train import train_jax
-        except Exception as exc:  # pragma: no cover
-            raise ImportError(f"Failed to import JAX trainer: {exc}") from exc
-        _, metrics = train_jax(cfg)
-    elif algo == "qtable":
-        _, metrics = train_qtable(cfg)
-    else:
-        _, metrics = train_sb3(cfg)
-    metrics["sweep/score"] = float(
-        metrics["eval/reward"] + float(cfg["revenue_weight"]) * metrics["eval/revenue"]
-    )
-    return metrics
-
-
-def run_wandb(
-    project: str, overrides: dict, mode: str = "online", sweep_mode: bool = False
-) -> dict:
-    if not HAS_WANDB:
-        raise ImportError("wandb is required for sweep runs")
-    if not sweep_mode:
-        pre_cfg = _cfg(overrides)
-        if pre_cfg.get("use_jax"):
-            try:
-                import jax
-
-                if jax.process_count() > 1 and jax.process_index() != 0:
-                    return train_once(pre_cfg)
-            except Exception:
-                pass
-    init_kwargs = {"mode": mode}
-    if sweep_mode:
-        run = wandb.init(**init_kwargs)
-    else:
-        run = wandb.init(project=project, config=overrides, **init_kwargs)
-
-    try:
-        cfg = _cfg(_wandb_cfg_dict())
-        if sweep_mode:
-            for k, v in overrides.items():
-                if k not in wandb.config:
-                    cfg[k] = v
-
-        metrics = train_once(cfg)
-        step = int(metrics.get("train/global_step", cfg["total_timesteps"]))
-        wandb.log(metrics, step=step)
-        for k, v in metrics.items():
-            run.summary[k] = v
-        return metrics
-    finally:
-        if wandb.run is not None:
-            wandb.finish()
-
-
-def run_local(overrides: dict) -> dict:
-    cfg = _cfg(overrides)
-    metrics = train_once(cfg)
-    should_print = True
-    if cfg.get("use_jax"):
-        try:
-            import jax
-
-            should_print = jax.process_index() == 0
-        except Exception:
-            should_print = True
-    if should_print:
-        print(json.dumps(metrics, indent=2))
-        # sentinel line for machine-readable extraction; must stay on one line
-        print("PHANTOM_METRICS:" + json.dumps(metrics))
-    return metrics
-
-
-def main():
-    p = argparse.ArgumentParser(description="PHANTOM training and W&B sweeps")
-    p.add_argument("--project", default=DEFAULT_CFG["project"])
-    p.add_argument("--algo", choices=["ppo", "a2c", "dqn", "qtable"])
-    p.add_argument("--seed", type=int)
-    p.add_argument("--total-timesteps", type=int)
-    p.add_argument("--alpha", type=float)
-    p.add_argument("--N", type=int)
-    p.add_argument("--n-products", type=int)
-    p.add_argument("--lambda-coi", type=float)
-    p.add_argument("--info-value", type=float)
-    p.add_argument("--robust-radius", type=float)
-    p.add_argument("--robust-points", type=int)
-    p.add_argument("--no-robust", action="store_true")
-    p.add_argument("--learning-rate", type=float)
-    p.add_argument("--gamma", type=float)
-    p.add_argument("--gae-lambda", type=float)
-    p.add_argument("--clip-range", type=float)
-    p.add_argument("--ent-coef", type=float)
-    p.add_argument("--revenue-weight", type=float)
-    p.add_argument("--price-low", type=float)
-    p.add_argument("--price-high", type=float)
-    p.add_argument("--action-levels", type=int)
-    p.add_argument("--action-scale-low", type=float)
-    p.add_argument("--action-scale-high", type=float)
-    p.add_argument("--max-steps", type=int)
-    p.add_argument("--margin-floor", type=float)
-    p.add_argument("--margin-floor-patience", type=int)
-    p.add_argument("--arch", type=str)
-    p.add_argument("--activation", type=str)
-    p.add_argument("--jax", action="store_true")
-    p.add_argument("--jax-num-envs", type=int)
-    p.add_argument("--jax-num-steps", type=int)
-    p.add_argument("--jax-num-minibatches", type=int)
-    p.add_argument("--jax-update-epochs", type=int)
-    p.add_argument("--jax-anneal-lr", type=str)
-    p.add_argument("--checkpoint-interval", type=int)
-    p.add_argument("--sweep-agent", action="store_true")
-    p.add_argument("--sweep-id", type=str)
-    p.add_argument("--count", type=int, default=0)
-    p.add_argument("--offline", action="store_true")
-    p.add_argument("--no-wandb", action="store_true")
-    args = p.parse_args()
+    backend = None if args.backend == "auto" else args.backend
 
     overrides = {
+        "project": args.project,
+        "backend": backend,
         "algo": args.algo,
         "seed": args.seed,
         "total_timesteps": args.total_timesteps,
+        "model_dir": args.model_dir,
+        "log_freq": args.log_freq,
+        "checkpoint_interval": args.checkpoint_interval,
+        "device": args.device,
         "alpha": args.alpha,
         "N": args.N,
         "n_products": args.n_products,
@@ -528,11 +150,6 @@ def main():
         "robust_radius": args.robust_radius,
         "robust_points": args.robust_points,
         "no_robust": args.no_robust,
-        "learning_rate": args.learning_rate,
-        "gamma": args.gamma,
-        "gae_lambda": args.gae_lambda,
-        "clip_range": args.clip_range,
-        "ent_coef": args.ent_coef,
         "revenue_weight": args.revenue_weight,
         "price_low": args.price_low,
         "price_high": args.price_high,
@@ -542,40 +159,87 @@ def main():
         "max_steps": args.max_steps,
         "margin_floor": args.margin_floor,
         "margin_floor_patience": args.margin_floor_patience,
+        "learning_rate": args.learning_rate,
+        "gamma": args.gamma,
+        "buffer_size": args.buffer_size,
+        "batch_size": args.batch_size,
+        "tau": args.tau,
+        "train_freq": args.train_freq,
+        "learning_starts": args.learning_starts,
+        "target_update_interval": args.target_update_interval,
+        "exploration_fraction": args.exploration_fraction,
+        "exploration_final_eps": args.exploration_final_eps,
+        "n_steps": args.n_steps,
+        "n_epochs": args.n_epochs,
+        "gae_lambda": args.gae_lambda,
+        "clip_range": args.clip_range,
+        "ent_coef": args.ent_coef,
+        "q_lr": args.q_lr,
+        "q_bins": args.q_bins,
+        "eps_start": args.eps_start,
+        "eps_end": args.eps_end,
+        "eps_decay": args.eps_decay,
         "arch": args.arch,
         "activation": args.activation,
-        "use_jax": args.jax,
+        "vf_coef": args.vf_coef,
+        "max_grad_norm": args.max_grad_norm,
+        "eval_freq": args.eval_freq,
+        "eval_episodes": args.eval_episodes,
+        "use_jax": args.jax or None,
         "jax_num_envs": args.jax_num_envs,
         "jax_num_steps": args.jax_num_steps,
         "jax_num_minibatches": args.jax_num_minibatches,
         "jax_update_epochs": args.jax_update_epochs,
-        "checkpoint_interval": args.checkpoint_interval,
-        "jax_anneal_lr": _truthy(args.jax_anneal_lr)
-        if args.jax_anneal_lr is not None
-        else None,
+        "jax_anneal_lr": jax_anneal_lr,
     }
-    overrides = {k: v for k, v in overrides.items() if v is not None}
+    return {key: value for key, value in overrides.items() if value is not None}
+
+
+def main(argv: list[str] | None = None) -> None:
+    import sys
+
+    raw_args = list(sys.argv[1:] if argv is None else argv)
+    run_kind = _probe_run_kind(raw_args)
+    if run_kind == "benchmark":
+        run_benchmark_cli(_strip_run_kind(raw_args))
+        return
+
+    parser = _build_parser()
+    args, unknown = parser.parse_known_args(raw_args)
+    if unknown:
+        raise ValueError(f"Unknown arguments for training mode: {' '.join(unknown)}")
+
+    overrides = _overrides_from_args(args)
+    scenario = str(args.scenario)
+    group = args.group
+    extra_tags = tuple(_parse_tags(args.tags))
 
     if args.sweep_agent:
-        if args.no_wandb:
-            raise ValueError("sweep agent requires wandb")
-        if not args.sweep_id:
-            raise ValueError("--sweep-id is required with --sweep-agent")
-        mode = "offline" if args.offline else "online"
-        wandb.agent(
-            args.sweep_id,
-            function=lambda: run_wandb(
-                args.project, overrides, mode=mode, sweep_mode=True
-            ),
-            count=args.count if args.count > 0 else None,
+        run_sweep_agent(
+            project=args.project,
+            sweep_id=str(args.sweep_id or ""),
+            count=int(args.count),
+            offline=bool(args.offline),
+            no_wandb=bool(args.no_wandb),
+            base_overrides=overrides,
+            kind="sweep",
+            scenario=scenario,
+            group=group,
+            extra_tags=extra_tags,
         )
         return
 
-    if args.no_wandb or not HAS_WANDB:
-        run_local(overrides)
-        return
-
-    run_wandb(args.project, overrides, mode="offline" if args.offline else "online")
+    spec = TrainSpec.from_flat(overrides)
+    run_train_once(
+        spec,
+        project=args.project,
+        offline=bool(args.offline),
+        no_wandb=bool(args.no_wandb),
+        kind="train",
+        scenario=scenario,
+        group=group,
+        extra_tags=extra_tags,
+    )
 
 
 if __name__ == "__main__":

engine/train_core.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from .spec import TrainSpec
+from .telemetry.metrics import canonicalize_metrics
+
+
+@dataclass(frozen=True)
+class TrainResult:
+    spec: TrainSpec
+    metrics: dict[str, Any]
+    artifacts: dict[str, str]
+
+
+def run_train(spec: TrainSpec) -> TrainResult:
+    cfg = spec.to_flat_dict()
+    algo = spec.algorithm.name
+
+    if spec.runtime.use_jax or spec.runtime.backend == "jax":
+        from .backends.jax import train_jax_backend
+
+        _, raw_metrics = train_jax_backend(cfg)
+    elif algo == "qtable":
+        from .backends.qtable import train_qtable
+
+        _, raw_metrics = train_qtable(cfg)
+    else:
+        from .backends.sb3 import train_sb3
+
+        _, raw_metrics = train_sb3(cfg)
+
+    metrics = canonicalize_metrics(raw_metrics, spec)
+    artifacts: dict[str, str] = {}
+    model_path = raw_metrics.get("model/path")
+    if isinstance(model_path, str):
+        artifacts["model/path"] = model_path
+
+    return TrainResult(spec=spec, metrics=metrics, artifacts=artifacts)

engine/wrapper.py

@@ -381,7 +381,7 @@ if __name__ == "__main__":
         def predict(self, obs, **kwargs):
             return self.env.action_space.sample(), None
 
-    wandb.init(project="phantom-pricing", config={"policy": "random", "alpha": 0.3})
+    wandb.init(project="capstone", config={"policy": "random", "alpha": 0.3})
     env = EconomicMetricsWrapper(PHANTOM(n_products=15, alpha=0.3, render_mode=None))
 
     model = RandomPolicy(env)

nx.json

@@ -55,6 +55,9 @@
     "train": {
       "cache": false
     },
+    "benchmark": {
+      "cache": false
+    },
     "up": {
       "cache": false
     },

package.json

@@ -19,6 +19,7 @@
     "platform:down": "nx run platform:down",
     "platform:logs": "nx run platform:logs",
     "research:test": "nx run research:test",
+    "research:benchmark": "nx run research:benchmark",
     "e2e:test": "nx run e2e:test"
   },
   "devDependencies": {

scripts/nx_research.sh

@@ -30,10 +30,20 @@ case "$cmd" in
     load_sweep_env
     require_var WANDB_API_KEY "WANDB_API_KEY required - set it in $env_file"
     WANDB_ENTITY="${WANDB_ENTITY:-}" \
-    WANDB_PROJECT="${WANDB_PROJECT:-phantom-pricing}" \
+    WANDB_PROJECT="${WANDB_PROJECT:-capstone}" \
     WANDB_API_KEY="$WANDB_API_KEY" \
       .venv/bin/python -m engine.train ${LOCAL_TRAIN_ARGS:---algo ppo --total-timesteps 50000}
     ;;
+  benchmark)
+    load_sweep_env
+    if [[ " ${LOCAL_BENCHMARK_ARGS:-} " != *" --no-wandb "* ]]; then
+      require_var WANDB_API_KEY "WANDB_API_KEY required - set it in $env_file"
+    fi
+    WANDB_ENTITY="${WANDB_ENTITY:-}" \
+    WANDB_PROJECT="${WANDB_PROJECT:-capstone}" \
+    WANDB_API_KEY="${WANDB_API_KEY:-}" \
+      .venv/bin/python -m engine.train --run-kind benchmark ${LOCAL_BENCHMARK_ARGS:---tiers static,surge,linear,qtable,ppo --alpha-values 0.0,0.3 --episodes 3 --total-timesteps 3000 --max-steps 40 --device cpu}
+    ;;
   train-agent)
     load_sweep_env
     require_var WANDB_API_KEY "WANDB_API_KEY required - set it in $env_file"
@@ -43,10 +53,23 @@ case "$cmd" in
       args+=(--count "$AGENT_COUNT")
     fi
     WANDB_ENTITY="${WANDB_ENTITY:-}" \
-    WANDB_PROJECT="${WANDB_PROJECT:-phantom-pricing}" \
+    WANDB_PROJECT="${WANDB_PROJECT:-capstone}" \
     WANDB_API_KEY="$WANDB_API_KEY" \
       .venv/bin/python -m engine.train "${args[@]}"
     ;;
+  benchmark-agent)
+    load_sweep_env
+    require_var WANDB_API_KEY "WANDB_API_KEY required - set it in $env_file"
+    require_var SWEEP_ID "SWEEP_ID required, e.g. SWEEP_ID=entity/project/id"
+    args=(--sweep-agent --sweep-id "$SWEEP_ID")
+    if [ -n "${AGENT_COUNT:-}" ] && [ "${AGENT_COUNT}" != "0" ]; then
+      args+=(--count "$AGENT_COUNT")
+    fi
+    WANDB_ENTITY="${WANDB_ENTITY:-}" \
+    WANDB_PROJECT="${WANDB_PROJECT:-capstone}" \
+    WANDB_API_KEY="$WANDB_API_KEY" \
+      .venv/bin/python -m engine.train --run-kind benchmark "${args[@]}" ${BENCHMARK_AGENT_ARGS:-}
+    ;;
   train-bootstrap)
     load_sweep_env
     require_var WANDB_API_KEY "WANDB_API_KEY required - set it in $env_file"
@@ -55,7 +78,7 @@ case "$cmd" in
     require_var SWEEP_ID "SWEEP_ID required, e.g. SWEEP_ID=entity/project/id"
     WANDB_API_KEY="$WANDB_API_KEY" \
     WANDB_ENTITY="${WANDB_ENTITY:-}" \
-    WANDB_PROJECT="${WANDB_PROJECT:-phantom-pricing}" \
+    WANDB_PROJECT="${WANDB_PROJECT:-capstone}" \
     GITHUB_TOKEN="$GITHUB_TOKEN" \
     REPO_URL="$REPO_URL" \
     BRANCH="${BRANCH:-main}" \
@@ -115,7 +138,7 @@ PY
   train-tpu-vm-sweep)
     load_sweep_env
     require_var TPU_NAME "TPU_NAME required, e.g. TPU_NAME=TPUlong"
-    require_var SWEEP_ID "SWEEP_ID required, e.g. SWEEP_ID=lusiana/phantom-pricing/abc123"
+    require_var SWEEP_ID "SWEEP_ID required, e.g. SWEEP_ID=lusiana/capstone/abc123"
     require_var WANDB_API_KEY "WANDB_API_KEY required - set it in $env_file"
     args=(
       --sweep-id "$SWEEP_ID"

scripts/tpu_vm_sweep_agent.py

@@ -96,7 +96,11 @@ def _extract_metrics(output: str) -> dict:
             obj = json.loads(block)
         except Exception:
             continue
-        if isinstance(obj, dict) and ("sweep/score" in obj or "eval/reward" in obj):
+        if isinstance(obj, dict) and (
+            "objective/score" in obj
+            or "eval/reward_mean" in obj
+            or "sweep/score" in obj
+        ):
             return obj
     return {}