mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
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, Any]]:
|
|
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"])
|
|
log_freq = max(1, int(cfg.get("log_freq", 100)))
|
|
obs, _ = env.reset(seed=int(cfg["seed"]))
|
|
|
|
interval_sums = {
|
|
"reward": 0.0,
|
|
"revenue": 0.0,
|
|
"agent_prob": 0.0,
|
|
"alpha_adv": 0.0,
|
|
"coi_leakage": 0.0,
|
|
}
|
|
interval_count = 0
|
|
train_events: list[dict[str, float | int]] = []
|
|
|
|
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)
|
|
revenue = float(info.get("economics", {}).get("revenue", 0.0))
|
|
total_revenue += revenue
|
|
steps += 1
|
|
interval_sums["reward"] += float(reward)
|
|
interval_sums["revenue"] += revenue
|
|
interval_sums["agent_prob"] += float(info.get("agent_prob", 0.0))
|
|
interval_sums["alpha_adv"] += float(info.get("alpha_adv", 0.0))
|
|
interval_sums["coi_leakage"] += float(info.get("coi_leakage", 0.0))
|
|
interval_count += 1
|
|
|
|
if steps % log_freq == 0 and 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),
|
|
}
|
|
)
|
|
interval_sums = {key: 0.0 for key in interval_sums}
|
|
interval_count = 0
|
|
|
|
epsilon = max(float(cfg["eps_end"]), epsilon * float(cfg["eps_decay"]))
|
|
obs = env.reset()[0] if done else nxt
|
|
|
|
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),
|
|
}
|
|
)
|
|
|
|
metrics: dict[str, Any] = {
|
|
"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"])))
|
|
metrics["_train_events"] = train_events
|
|
|
|
env.close()
|
|
eval_env.close()
|
|
return agent, metrics
|