Spaces:
Running
Running
File size: 10,704 Bytes
df97e68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 | """
Custom SB3 callbacks for Gov Workflow RL training.
GovWorkflowEvalCallback -- MaskableEvalCallback + grader score logging
CostMonitorCallback -- per-rollout cost constraint logging to TensorBoard
"""
from __future__ import annotations
import os
import numpy as np
from stable_baselines3.common.callbacks import BaseCallback
from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback
from typing import Any
from rl.gov_workflow_env import GovWorkflowGymEnv
from rl.cost_tracker import THRESHOLD_SLA, THRESHOLD_FAIRNESS
class GovWorkflowEvalCallback(MaskableEvalCallback):
"""
Extends MaskableEvalCallback:
1. Runs the deterministic grader after each eval.
2. Logs grader score to TensorBoard.
3. Saves best model by grader score (not just mean reward).
"""
def __init__(
self,
eval_env: GovWorkflowGymEnv,
eval_freq: int = 2048,
n_eval_episodes: int = 5,
grader_eval_freq_multiplier: int = 4,
grader_eval_max_steps: int | None = None,
best_model_save_path: str = "results/best_model",
log_path: str = "results/eval_logs",
task_id: str = "district_backlog_easy",
verbose: int = 1,
):
super().__init__(
eval_env=eval_env,
n_eval_episodes=n_eval_episodes,
eval_freq=eval_freq,
best_model_save_path=best_model_save_path,
log_path=log_path,
verbose=verbose,
warn=False,
)
self.task_id = task_id
self.grader_eval_freq_multiplier = max(1, int(grader_eval_freq_multiplier))
self.grader_eval_max_steps = grader_eval_max_steps
self._best_grader_score = -np.inf
os.makedirs(best_model_save_path, exist_ok=True)
os.makedirs(log_path, exist_ok=True)
def _on_step(self) -> bool:
eval_due = self.eval_freq > 0 and self.n_calls % self.eval_freq == 0
result = super()._on_step()
if eval_due:
mean_reward = float(getattr(self, "last_mean_reward", 0.0) or 0.0)
std_reward = 0.0
try:
if self.evaluations_results and len(self.evaluations_results) > 0:
latest = self.evaluations_results[-1]
if latest is not None and len(latest) > 0:
std_reward = float(np.std(latest))
except Exception:
std_reward = 0.0
# Stable line format for live parser in backend/frontend.
print(
f"Eval num_timesteps={int(self.num_timesteps)}, "
f"episode_reward={mean_reward:.2f} +/- {std_reward:.2f}",
flush=True,
)
grader_eval_freq = max(self.eval_freq * self.grader_eval_freq_multiplier, 1)
if self.eval_freq > 0 and self.n_calls % grader_eval_freq == 0:
grader_score = self._run_grader_eval()
if self.logger:
self.logger.record("eval/grader_score", grader_score)
if grader_score > self._best_grader_score:
self._best_grader_score = grader_score
save_path = os.path.join(
self.best_model_save_path, f"best_grader_{self.task_id}"
)
self.model.save(save_path)
if self.verbose:
print(f"[Eval] New best grader score: {grader_score:.4f} -> {save_path}")
return result
def _run_grader_eval(self) -> float:
try:
from app.graders import grade_episode
from app.tasks import TASKS
task_cfg = TASKS.get(self.task_id)
if task_cfg is None:
return 0.0
max_steps = (
int(self.grader_eval_max_steps)
if self.grader_eval_max_steps is not None
else max(1, int(task_cfg.max_days) * 10)
)
env = GovWorkflowGymEnv(task_id=self.task_id, seed=task_cfg.seed, hard_action_mask=True)
obs, _ = env.reset()
done = False
steps = 0
while not done:
masks = np.asarray(env.action_masks(), dtype=bool).reshape(-1)
action, _ = self.model.predict(obs, action_masks=masks, deterministic=True)
obs, _, terminated, truncated, _ = env.step(int(action))
done = terminated or truncated
steps += 1
if steps >= max_steps and not done:
break
result = grade_episode(env._core_env.state())
return float(result.score)
except Exception as e:
if self.verbose:
print(f"[Eval] Grader eval failed: {e}")
return 0.0
class CostMonitorCallback(BaseCallback):
"""
Monitors SLA and fairness cost signals per rollout.
Phase 1-3: diagnostic only.
Phase 4: feeds into Lagrangian multiplier updates.
"""
def __init__(self, verbose: int = 0):
super().__init__(verbose)
self._episode_costs: list[dict] = []
self._ep_sla: list[float] = []
self._ep_fair: list[float] = []
self._ep_mask_applied: list[float] = []
def _on_step(self) -> bool:
for info, done in zip(
self.locals.get("infos", []),
self.locals.get("dones", []),
):
rb = info.get("reward_breakdown", {})
self._ep_sla.append( abs(float(rb.get("sla_penalty", 0.0))))
self._ep_fair.append(abs(float(rb.get("fairness_penalty", 0.0))))
self._ep_mask_applied.append(float(bool(info.get("action_mask_applied", False))))
if done:
mean_sla = float(np.mean(self._ep_sla)) if self._ep_sla else 0.0
mean_fair = float(np.mean(self._ep_fair)) if self._ep_fair else 0.0
mask_rate = float(np.mean(self._ep_mask_applied)) if self._ep_mask_applied else 0.0
self._episode_costs.append({"sla": mean_sla, "fairness": mean_fair})
self.logger.record("costs/episode_mean_sla_penalty", mean_sla)
self.logger.record("costs/episode_mean_fairness_penalty", mean_fair)
self.logger.record("costs/sla_threshold_violated", float(mean_sla > THRESHOLD_SLA))
self.logger.record("costs/fairness_threshold_violated", float(mean_fair > THRESHOLD_FAIRNESS))
self.logger.record("costs/episode_action_mask_applied_rate", mask_rate)
self._ep_sla.clear()
self._ep_fair.clear()
self._ep_mask_applied.clear()
return True
def _on_training_end(self) -> None:
if not self._episode_costs:
return
all_sla = [c["sla"] for c in self._episode_costs]
all_fair = [c["fairness"] for c in self._episode_costs]
print(
f"\n[CostMonitor] mean SLA penalty: {np.mean(all_sla):.4f} "
f"(threshold={THRESHOLD_SLA}), "
f"mean fairness penalty: {np.mean(all_fair):.4f} "
f"(threshold={THRESHOLD_FAIRNESS})"
)
class RecurrentEvalCallback(BaseCallback):
"""
Periodic evaluation callback for RecurrentPPO.
We evaluate with deterministic inference and enforce action masks at
inference time before env.step().
"""
def __init__(
self,
eval_env: GovWorkflowGymEnv,
eval_freq: int = 2048,
n_eval_episodes: int = 3,
best_model_save_path: str = "results/best_model",
log_path: str = "results/eval_logs",
task_id: str = "mixed_urgency_medium",
verbose: int = 1,
):
super().__init__(verbose=verbose)
self.eval_env = eval_env
self.eval_freq = eval_freq
self.n_eval_episodes = n_eval_episodes
self.best_model_save_path = best_model_save_path
self.log_path = log_path
self.task_id = task_id
self._best_grader_score = -np.inf
os.makedirs(best_model_save_path, exist_ok=True)
os.makedirs(log_path, exist_ok=True)
def _on_step(self) -> bool:
if self.eval_freq <= 0 or self.n_calls % self.eval_freq != 0:
return True
mean_reward, grader_score = self._run_eval()
self.logger.record("eval/mean_reward", mean_reward)
self.logger.record("eval/grader_score", grader_score)
if grader_score > self._best_grader_score:
self._best_grader_score = grader_score
save_path = os.path.join(
self.best_model_save_path, f"best_grader_recurrent_{self.task_id}"
)
self.model.save(save_path)
if self.verbose:
print(f"[Eval] New best recurrent grader score: {grader_score:.4f} -> {save_path}")
return True
def _run_eval(self) -> tuple[float, float]:
from app.graders import grade_episode
from app.tasks import TASKS
task_cfg = TASKS.get(self.task_id)
if task_cfg is None:
return 0.0, 0.0
rewards: list[float] = []
scores: list[float] = []
for ep in range(self.n_eval_episodes):
env = GovWorkflowGymEnv(self.task_id, seed=task_cfg.seed + ep, hard_action_mask=True)
obs, _ = env.reset()
done = False
ep_reward = 0.0
lstm_state: Any = None
episode_start = np.array([True], dtype=bool)
while not done:
action, lstm_state = self.model.predict(
obs,
state=lstm_state,
episode_start=episode_start,
deterministic=True,
)
action_idx = int(np.asarray(action).item())
masks = env.action_masks()
if action_idx < 0 or action_idx >= masks.shape[0] or not bool(masks[action_idx]):
if masks.shape[0] > 18 and bool(masks[18]):
action_idx = 18
else:
valid = np.flatnonzero(masks)
if valid.size > 0:
action_idx = int(valid[0])
obs, reward, terminated, truncated, _ = env.step(action_idx)
ep_reward += float(reward)
done = bool(terminated or truncated)
episode_start = np.array([done], dtype=bool)
result = grade_episode(env._core_env.state())
rewards.append(ep_reward)
scores.append(float(result.score))
return float(np.mean(rewards)), float(np.mean(scores))
|