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feature: telemetry logging

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This commit is contained in:
Daniel Rosel
2026-03-10 14:23:17 +01:00
parent be03b2d4d5
commit 4c7d911043
14 changed files with 454 additions and 104 deletions
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35
engine/backends/qtable.py
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@@ -7,6 +7,7 @@ from typing import Any, Mapping
import numpy as np
from .common import evaluate, make_env
from ..telemetry.wandb import get_wandb_module
logger = logging.getLogger(__name__)
@@ -36,6 +37,9 @@ def train_qtable(
console_progress = bool(cfg.get("console_progress", False))
obs, _ = env.reset(seed=int(cfg["seed"]))
started_at = time.perf_counter()
wandb = get_wandb_module()
wandb_live = bool(wandb is not None and wandb.run is not None)
step_offset = max(0, int(cfg.get("wandb_step_offset", 0)))
interval_sums = {
"reward": 0.0,
@@ -75,7 +79,10 @@ def train_qtable(
"train/epsilon": float(epsilon),
"train/global_step": int(steps),
}
train_events.append(event)
if wandb_live:
wandb.log(dict(event), step=step_offset + int(steps))
else:
train_events.append(event)
if console_progress:
elapsed = max(time.perf_counter() - started_at, 1e-6)
speed = steps / elapsed
@@ -96,17 +103,19 @@ def train_qtable(
if interval_count > 0:
denom = float(interval_count)
train_events.append(
{
"train/reward_mean": interval_sums["reward"] / denom,
"train/revenue_mean": interval_sums["revenue"] / denom,
"train/agent_prob": interval_sums["agent_prob"] / denom,
"train/alpha_adv": interval_sums["alpha_adv"] / denom,
"train/coi_leakage": interval_sums["coi_leakage"] / denom,
"train/epsilon": float(epsilon),
"train/global_step": int(steps),
}
)
tail_event = {
"train/reward_mean": interval_sums["reward"] / denom,
"train/revenue_mean": interval_sums["revenue"] / denom,
"train/agent_prob": interval_sums["agent_prob"] / denom,
"train/alpha_adv": interval_sums["alpha_adv"] / denom,
"train/coi_leakage": interval_sums["coi_leakage"] / denom,
"train/epsilon": float(epsilon),
"train/global_step": int(steps),
}
if wandb_live:
wandb.log(dict(tail_event), step=step_offset + int(steps))
else:
train_events.append(tail_event)
metrics: dict[str, Any] = {
"train/reward_mean": total_reward / max(steps, 1),
@@ -114,7 +123,7 @@ def train_qtable(
"train/epsilon": float(epsilon),
"train/global_step": int(cfg["total_timesteps"]),
}
metrics.update(evaluate(agent, eval_env, int(cfg["eval_episodes"])))
metrics.update(evaluate(agent, eval_env, int(cfg["eval_episodes"]), cfg=cfg))
metrics["_train_events"] = train_events
env.close()