diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index f849f64..c0227bb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -151,8 +151,11 @@ jobs: - name: Run container and check /health run: | # Start the API in the background; it will boot in degraded mode - # since no policy is mounted, but /health should still respond. - docker run -d --name api-smoke -p 8000:8000 food-rescue-serve:smoke + # since no policy is mounted and MLflow is not available in CI. + # FOOD_RESCUE_DISABLE_MLFLOW_REGISTRY=1 prevents hanging on MLflow connection. + docker run -d --name api-smoke \ + -e FOOD_RESCUE_DISABLE_MLFLOW_REGISTRY=1 \ + -p 8000:8000 food-rescue-serve:smoke # Give uvicorn ~10s to come up for i in $(seq 1 30); do if curl -fsS http://localhost:8000/health > /dev/null; then diff --git a/agents/dqn.py b/agents/dqn.py index cfa24be..b4bb21c 100644 --- a/agents/dqn.py +++ b/agents/dqn.py @@ -84,31 +84,49 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: # ----------------------------- class ReplayBuffer: - """Fixed-size circular buffer of (s, a, r, s', done) tuples.""" + """Fixed-size circular buffer of (s, a, r, s', done, next_mask) tuples. - def __init__(self, capacity: int, seed: Optional[int] = None): + The next_mask field is a boolean array of legal actions in the next state. + It enables training-time action masking in the Bellman target: we only + take max over valid next-state actions, so the target isn't polluted by + Q-values of actions the env wouldn't allow. + + Older transitions stored without a mask are auto-padded to all-True for + backward compatibility with checkpoints / replay snapshots saved before v5. + """ + + def __init__(self, capacity: int, num_actions: int, seed: Optional[int] = None): self._buf: deque = deque(maxlen=capacity) self._rng = random.Random(seed) + self.num_actions = num_actions def push(self, obs: np.ndarray, action: int, reward: float, - next_obs: np.ndarray, done: bool) -> None: - self._buf.append((obs, action, reward, next_obs, done)) + next_obs: np.ndarray, done: bool, + next_mask: Optional[np.ndarray] = None) -> None: + self._buf.append((obs, action, reward, next_obs, done, next_mask)) def sample(self, batch_size: int) -> tuple[np.ndarray, ...]: batch = self._rng.sample(self._buf, batch_size) - obs, actions, rewards, next_obs, dones = zip(*batch) + obs, actions, rewards, next_obs, dones, next_masks = zip(*batch) + + # Pad missing masks with all-True so we don't break legacy training + padded_masks = [ + m if m is not None else np.ones(self.num_actions, dtype=bool) + for m in next_masks + ] + return ( np.stack(obs).astype(np.float32), np.array(actions, dtype=np.int64), np.array(rewards, dtype=np.float32), np.stack(next_obs).astype(np.float32), np.array(dones, dtype=np.float32), + np.stack(padded_masks).astype(bool), ) def __len__(self) -> int: return len(self._buf) - # ----------------------------- # DQN Agent # ----------------------------- @@ -138,6 +156,11 @@ def __init__( self.config = config if config is not None else DQNConfig() self.obs_dim = obs_dim self.num_actions = num_actions + self.replay = ReplayBuffer( + capacity=self.config.replay_buffer_size, + num_actions=num_actions, + seed=seed, + ) if seed is not None: torch.manual_seed(seed) @@ -153,7 +176,6 @@ def __init__( self.target_net.eval() self.optimizer = optim.Adam(self.q_net.parameters(), lr=self.config.learning_rate) - self.replay = ReplayBuffer(self.config.replay_buffer_size, seed=seed) self._step_count = 0 # global step counter (for target updates) self._episode_count = 0 # for ε annealing @@ -169,47 +191,73 @@ def epsilon(self) -> float: return c.epsilon_start + (c.epsilon_end - c.epsilon_start) * progress def select_action(self, env: FoodRescueEnv, obs: np.ndarray) -> int: + # Get action mask from env (or use all-valid if env doesn't support it, + # to maintain backward compat with older code paths) + if hasattr(env, "action_mask"): + mask = env.action_mask() + else: + mask = np.ones(self.num_actions, dtype=bool) + + valid_actions = np.flatnonzero(mask) + eps = self.epsilon() if self._training else 0.0 if self._training and self._np_rng.random() < eps: - return int(self._np_rng.integers(0, self.num_actions)) + # Random exploration, but only among valid actions + return int(self._np_rng.choice(valid_actions)) with torch.no_grad(): obs_t = torch.from_numpy(obs.astype(np.float32)).unsqueeze(0).to(self.device) q_values = self.q_net(obs_t).squeeze(0).cpu().numpy() - # Random tie-breaking - max_val = q_values.max() - best = np.flatnonzero(q_values == max_val) - return int(self._np_rng.choice(best)) + # Mask out invalid actions by setting their Q-values to -inf + masked_q = np.where(mask, q_values, -np.inf) + + # Random tie-breaking among the best valid actions + max_val = masked_q.max() + best = np.flatnonzero(masked_q == max_val) + return int(self._np_rng.choice(best)) # ---- Training ---- - def store_transition(self, obs, action, reward, next_obs, done) -> None: - self.replay.push(obs, action, reward, next_obs, done) + def store_transition(self, obs, action, reward, next_obs, done, next_mask=None) -> None: + self.replay.push(obs, action, reward, next_obs, done, next_mask) def train_step(self) -> Optional[float]: """ One gradient step on a sampled batch. Returns the loss, or None if the replay buffer is too small to train yet. + + Uses action masking in the Bellman target: max_a' Q(s', a') is taken + only over actions that are valid in s'. This sharpens credit + assignment when the env has structural action constraints. """ if not self._training: return None if len(self.replay) < self.config.min_replay_to_train: return None - obs_b, act_b, rew_b, next_obs_b, done_b = self.replay.sample(self.config.batch_size) + obs_b, act_b, rew_b, next_obs_b, done_b, next_mask_b = self.replay.sample( + self.config.batch_size + ) obs_t = torch.from_numpy(obs_b).to(self.device) act_t = torch.from_numpy(act_b).to(self.device) rew_t = torch.from_numpy(rew_b).to(self.device) next_obs_t = torch.from_numpy(next_obs_b).to(self.device) done_t = torch.from_numpy(done_b).to(self.device) + # Convert mask to a float tensor that we'll use to set invalid Q-values + # to a very negative number before taking the max. + next_mask_t = torch.from_numpy(next_mask_b).to(self.device) # Current Q(s, a) q_pred = self.q_net(obs_t).gather(1, act_t.unsqueeze(1)).squeeze(1) - # Bootstrap target: r + gamma * max_a' Q_target(s', a'), zeroed at terminal + # Bootstrap target: r + gamma * max_a' Q_target(s', a'), masked. + # Invalid actions get -1e9 so they never win the argmax. with torch.no_grad(): - q_next_max = self.target_net(next_obs_t).max(dim=1).values + q_next = self.target_net(next_obs_t) + # Where mask is False, replace Q with -inf (very negative float). + q_next_masked = q_next.masked_fill(~next_mask_t, -1e9) + q_next_max = q_next_masked.max(dim=1).values target = rew_t + self.config.discount * q_next_max * (1.0 - done_t) loss = nn.functional.smooth_l1_loss(q_pred, target) @@ -219,6 +267,7 @@ def train_step(self) -> Optional[float]: torch.nn.utils.clip_grad_norm_(self.q_net.parameters(), self.config.grad_clip) self.optimizer.step() + # Periodic target network sync self._step_count += 1 if self._step_count % self.config.target_update_interval == 0: self.target_net.load_state_dict(self.q_net.state_dict()) diff --git a/api/policy_loader.py b/api/policy_loader.py index c774566..4683cf4 100644 --- a/api/policy_loader.py +++ b/api/policy_loader.py @@ -5,8 +5,9 @@ 1. From the MLflow Model Registry (production-style): set FOOD_RESCUE_MODEL_NAME and FOOD_RESCUE_MODEL_VERSION env vars -2. From a local file path: set FOOD_RESCUE_MODEL_PATH -3. Built-in default: look for experiments/policies/dqn_tuned.pt +2. From the MLflow Model Registry defaults: food_rescue_dqn @ latest +3. From a local file path: set FOOD_RESCUE_MODEL_PATH +4. Built-in fallback: look for experiments/policies/dqn_v5_masked.pt (or any DQN policy file in that folder) Only DQN policies are supported for serving — they take an obs vector directly, @@ -66,7 +67,11 @@ def _load_from_mlflow_registry( # Download the artifact directory local_dir = mlflow.artifacts.download_artifacts(source_uri) - return _load_dqn_from_dir(Path(local_dir), source=f"mlflow:{model_name}:{version}") + agent, info = _load_dqn_from_dir(Path(local_dir), source=f"mlflow:{model_name}:{version}") + # Override the cosmetic fields so /info reflects the real registry version + info["model_name"] = model_name + info["model_version"] = str(version) + return agent, info def _load_from_path(path: str) -> tuple[DQNAgent, dict[str, Any]]: @@ -129,7 +134,10 @@ def load_policy_from_env() -> tuple[DQNAgent, dict[str, Any]]: Resolution order: 1. FOOD_RESCUE_MODEL_NAME + FOOD_RESCUE_MODEL_VERSION -> MLflow Registry 2. FOOD_RESCUE_MODEL_PATH -> local file or directory - 3. Default: experiments/policies/dqn_tuned.pt or dqn_v1.pt + 3. Default MLflow Registry model food_rescue_dqn @ latest (skipped if unavailable) + 4. Local DQN fallback files + + Raises FileNotFoundError if no policy can be loaded via any method. """ model_name = os.environ.get("FOOD_RESCUE_MODEL_NAME") model_version = os.environ.get("FOOD_RESCUE_MODEL_VERSION") @@ -141,18 +149,30 @@ def load_policy_from_env() -> tuple[DQNAgent, dict[str, Any]]: if model_path: return _load_from_path(model_path) - # Fallback: look for any DQN policy + # Try MLflow registry default, but don't fail if it's unavailable (e.g., CI environment) + # Can be disabled entirely with FOOD_RESCUE_DISABLE_MLFLOW_REGISTRY=1 + if not os.environ.get("FOOD_RESCUE_DISABLE_MLFLOW_REGISTRY"): + try: + return _load_from_mlflow_registry("food_rescue_dqn", "latest") + except Exception as e: + print(f" MLflow registry not available ({type(e).__name__}), trying local fallback...") + else: + print(" MLflow registry disabled via FOOD_RESCUE_DISABLE_MLFLOW_REGISTRY") + + # Fallback: look for any DQN policy on local disk candidates = [ + Path("experiments/policies/dqn_v5_masked.pt"), Path("experiments/policies/dqn_tuned.pt"), Path("experiments/policies/dqn_v3_normalized.pt"), ] for c in candidates: if c.exists(): + print(f" Found local policy: {c}") return _load_from_path(str(c)) raise FileNotFoundError( - "No policy could be loaded. Set FOOD_RESCUE_MODEL_NAME + " - "FOOD_RESCUE_MODEL_VERSION (for MLflow registry), or " - "FOOD_RESCUE_MODEL_PATH (for local file), or place a DQN policy at " - "experiments/policies/dqn_tuned.pt or dqn_v1.pt." + "No policy could be loaded. Please set one of:\n" + " - FOOD_RESCUE_MODEL_NAME + FOOD_RESCUE_MODEL_VERSION (MLflow registry)\n" + " - FOOD_RESCUE_MODEL_PATH (local file or directory)\n" + " - Place a DQN policy at experiments/policies/dqn_v5_masked.pt or similar" ) diff --git a/configs/dqn_v4_dense.yaml b/configs/dqn_v4_dense.yaml new file mode 100644 index 0000000..855c4c5 --- /dev/null +++ b/configs/dqn_v4_dense.yaml @@ -0,0 +1,59 @@ +run: + run_id: dqn_v4_dense + agent: dqn + scenario: weekday + num_episodes: 1500 + seed: 42 + output_dir: experiments + description: | + v4: dense pickup signal + γ back to 0.95 + higher ε floor. + + Diagnosis of v3 (see scripts/eval_dqn_in_python.py output): + - Q-values collapsed to a flat ~18-20 across all 11 actions + - Average q-spread per step: 1.19 (healthy is 5-15) + - Step 0 Q-values identical across 5 different random seeds + - Model treated every action as equally good; survived only by + env forgiveness and random tie-breaking + - Root cause: γ=0.99 with sparse delivery-only reward smoothed + value across all states; the network learned ONE mean value + + v4 fixes: + - Add pickup reward (dense intermediate signal at 0.2 per unit + loaded). Breaks the value-smoothing symmetry — picking up at a + donor with food is now a learnable positive event, not just a + precursor to delivery. + - γ=0.95 (was 0.99). Preserves discrimination between near-term + and far-term consequences. With 200-step episodes, γ=0.95 + values reward 14 steps out at ~50% — appropriate horizon. + - ε_end=0.10 (was 0.02). Keep exploring throughout training; + model needs to discover non-greedy good actions. + - min_replay_to_train=1000 (was 5000). Start learning sooner. + +agent_params: + hidden_sizes: [128, 128] + learning_rate: 0.0005 + discount: 0.95 + epsilon_start: 1.0 + epsilon_end: 0.10 + epsilon_decay_episodes: 1200 + replay_buffer_size: 100000 + batch_size: 64 + min_replay_to_train: 1000 + target_update_interval: 500 + grad_clip: 1.0 + device: auto + +# Normalized scale (delivery=1, not 10) keeps DQN TD targets bounded. +# New pickup reward gives the dense intermediate signal v3 was missing. +reward_weights: + delivery: 1.0 + pickup: 0.2 + spoilage: 0.5 + distance: 0.01 + unmet_demand: 0.1 + priority_bonus: 0.05 + oversupply_penalty: 0.03 + +eval: + n_episodes: 5 + eval_seeds: [100, 101, 102, 103, 104] diff --git a/configs/dqn_v5_masked.yaml b/configs/dqn_v5_masked.yaml new file mode 100644 index 0000000..48a7973 --- /dev/null +++ b/configs/dqn_v5_masked.yaml @@ -0,0 +1,51 @@ +run: + run_id: dqn_v5_masked + agent: dqn + scenario: weekday + num_episodes: 1500 + seed: 42 + output_dir: experiments + description: | + v5: DQN with full training-time action masking. + + Diagnosis of v4: pickup reward alone didn't break the flat-Q problem. + Inference-time masking on v4 (no retraining) lifted delivered from 75 + to 85, proving the constraint mattered. But Q-values stayed flat + (spread 1.16) because training itself was polluted: the agent spent + many episodes taking actions the env would convert to idle. + + v5 fixes: + - Action mask consulted during action selection (already in v4 at + inference; now also at training-time exploration) + - Bellman target uses max_a' Q(s', a') taken only over a' valid in s' + - Replay buffer stores next_state mask alongside each transition + + Hyperparameters unchanged from v4 to make the comparison clean — + the only variable is masking. Any improvement is attributable to it. + +agent_params: + hidden_sizes: [128, 128] + learning_rate: 0.0005 + discount: 0.95 + epsilon_start: 1.0 + epsilon_end: 0.10 + epsilon_decay_episodes: 1200 + replay_buffer_size: 100000 + batch_size: 64 + min_replay_to_train: 1000 + target_update_interval: 500 + grad_clip: 1.0 + device: auto + +reward_weights: + delivery: 1.0 + pickup: 0.2 + spoilage: 0.5 + distance: 0.01 + unmet_demand: 0.1 + priority_bonus: 0.05 + oversupply_penalty: 0.03 + +eval: + n_episodes: 5 + eval_seeds: [100, 101, 102, 103, 104] diff --git a/experiments/policies/dqn_v4_dense.meta.json b/experiments/policies/dqn_v4_dense.meta.json new file mode 100644 index 0000000..318b64f --- /dev/null +++ b/experiments/policies/dqn_v4_dense.meta.json @@ -0,0 +1,23 @@ +{ + "config": { + "hidden_sizes": [ + 128, + 128 + ], + "learning_rate": 0.0005, + "discount": 0.95, + "epsilon_start": 1.0, + "epsilon_end": 0.1, + "epsilon_decay_episodes": 1200, + "replay_buffer_size": 100000, + "batch_size": 64, + "min_replay_to_train": 1000, + "target_update_interval": 500, + "grad_clip": 1.0, + "device": "auto" + }, + "obs_dim": 31, + "num_actions": 11, + "step_count": 299001, + "episode_count": 1500 +} \ No newline at end of file diff --git a/experiments/policies/dqn_v4_dense.pt b/experiments/policies/dqn_v4_dense.pt new file mode 100644 index 0000000..141f012 Binary files /dev/null and b/experiments/policies/dqn_v4_dense.pt differ diff --git a/experiments/policies/dqn_v5_masked.meta.json b/experiments/policies/dqn_v5_masked.meta.json new file mode 100644 index 0000000..318b64f --- /dev/null +++ b/experiments/policies/dqn_v5_masked.meta.json @@ -0,0 +1,23 @@ +{ + "config": { + "hidden_sizes": [ + 128, + 128 + ], + "learning_rate": 0.0005, + "discount": 0.95, + "epsilon_start": 1.0, + "epsilon_end": 0.1, + "epsilon_decay_episodes": 1200, + "replay_buffer_size": 100000, + "batch_size": 64, + "min_replay_to_train": 1000, + "target_update_interval": 500, + "grad_clip": 1.0, + "device": "auto" + }, + "obs_dim": 31, + "num_actions": 11, + "step_count": 299001, + "episode_count": 1500 +} \ No newline at end of file diff --git a/experiments/policies/dqn_v5_masked.pt b/experiments/policies/dqn_v5_masked.pt new file mode 100644 index 0000000..a7fac19 Binary files /dev/null and b/experiments/policies/dqn_v5_masked.pt differ diff --git a/experiments/prediction_log.db b/experiments/prediction_log.db index d403d3c..b6f3b80 100644 Binary files a/experiments/prediction_log.db and b/experiments/prediction_log.db differ diff --git a/experiments/results/dqn_v4_dense.csv b/experiments/results/dqn_v4_dense.csv new file mode 100644 index 0000000..2c103da --- /dev/null +++ b/experiments/results/dqn_v4_dense.csv @@ -0,0 +1,1501 @@ +episode,total_reward,steps,epsilon,replay_size,mean_loss,delivered_units,spoiled_units,deliveries_count,distance +0,-159.53100000000003,200,0.99925,200,0.0,6.0,359.0,1,178 +1,-159.7755999999999,200,0.9985,400,0.0,10.0,357.0,2,177 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--git a/experiments/results/dqn_v4_dense_meta.json b/experiments/results/dqn_v4_dense_meta.json new file mode 100644 index 0000000..df7ed4c --- /dev/null +++ b/experiments/results/dqn_v4_dense_meta.json @@ -0,0 +1,105 @@ +{ + "run_id": "dqn_v4_dense", + "agent": "dqn", + "scenario": "weekday", + "num_episodes": 1500, + "seed": 42, + "git_commit": "ba3b051a87c71a0dd6db53d8e6d7b0bf5edd312f", + "wall_time_seconds": 2244.8660871982574, + "eval_summary": { + "eval_mean_reward": 13.598280000000006, + "eval_std_reward": 40.917405917501654, + "eval_mean_delivered": 74.6, + "eval_mean_spoiled": 197.0, + "eval_n_episodes": 5 + }, + "eval_per_seed": [ + { + "seed": 100, + "total_reward": -1.0044000000000124, + "delivered_units": 75.0, + "spoiled_units": 230.0, + "deliveries_count": 24, + "distance": 155 + }, + { + "seed": 101, + "total_reward": 1.4250000000000504, + "delivered_units": 63.0, + "spoiled_units": 186.0, + "deliveries_count": 25, + "distance": 176 + }, + { + "seed": 102, + "total_reward": -36.79879999999999, + "delivered_units": 57.0, + "spoiled_units": 257.0, + "deliveries_count": 29, + "distance": 153 + }, + { + "seed": 103, + "total_reward": 87.48379999999997, + "delivered_units": 123.0, + "spoiled_units": 168.0, + "deliveries_count": 35, + "distance": 164 + }, + { + "seed": 104, + "total_reward": 16.885800000000014, + "delivered_units": 55.0, + "spoiled_units": 144.0, + "deliveries_count": 22, + "distance": 158 + } + ], + "config_raw": { + "run": { + "run_id": "dqn_v4_dense", + "agent": "dqn", + "scenario": "weekday", + "num_episodes": 1500, + "seed": 42, + "output_dir": "experiments", + "description": "v4: dense pickup signal + \u03b3 back to 0.95 + higher \u03b5 floor.\n\nDiagnosis of v3 (see scripts/eval_dqn_in_python.py output):\n- Q-values collapsed to a flat ~18-20 across all 11 actions\n- Average q-spread per step: 1.19 (healthy is 5-15)\n- Step 0 Q-values identical across 5 different random seeds\n- Model treated every action as equally good; survived only by\n env forgiveness and random tie-breaking\n- Root cause: \u03b3=0.99 with sparse delivery-only reward smoothed\n value across all states; the network learned ONE mean value\n\nv4 fixes:\n- Add pickup reward (dense intermediate signal at 0.2 per unit\n loaded). Breaks the value-smoothing symmetry \u2014 picking up at a\n donor with food is now a learnable positive event, not just a\n precursor to delivery.\n- \u03b3=0.95 (was 0.99). Preserves discrimination between near-term\n and far-term consequences. With 200-step episodes, \u03b3=0.95\n values reward 14 steps out at ~50% \u2014 appropriate horizon.\n- \u03b5_end=0.10 (was 0.02). Keep exploring throughout training;\n model needs to discover non-greedy good actions.\n- min_replay_to_train=1000 (was 5000). Start learning sooner.\n" + }, + "agent_params": { + "hidden_sizes": [ + 128, + 128 + ], + "learning_rate": 0.0005, + "discount": 0.95, + "epsilon_start": 1.0, + "epsilon_end": 0.1, + "epsilon_decay_episodes": 1200, + "replay_buffer_size": 100000, + "batch_size": 64, + "min_replay_to_train": 1000, + "target_update_interval": 500, + "grad_clip": 1.0, + "device": "auto" + }, + "reward_weights": { + "delivery": 1.0, + "pickup": 0.2, + "spoilage": 0.5, + "distance": 0.01, + "unmet_demand": 0.1, + "priority_bonus": 0.05, + "oversupply_penalty": 0.03 + }, + "eval": { + "n_episodes": 5, + "eval_seeds": [ + 100, + 101, + 102, + 103, + 104 + ] + } + } +} \ No newline at end of file diff --git a/experiments/results/dqn_v5_masked.csv b/experiments/results/dqn_v5_masked.csv new file mode 100644 index 0000000..4438c58 --- /dev/null +++ b/experiments/results/dqn_v5_masked.csv @@ -0,0 +1,1501 @@ 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+1499,68.8088,200,0.1,100000,0.8609820802509784,83.0,122.0,39,188 diff --git a/experiments/results/dqn_v5_masked_meta.json b/experiments/results/dqn_v5_masked_meta.json new file mode 100644 index 0000000..1f19a74 --- /dev/null +++ b/experiments/results/dqn_v5_masked_meta.json @@ -0,0 +1,105 @@ +{ + "run_id": "dqn_v5_masked", + "agent": "dqn", + "scenario": "weekday", + "num_episodes": 1500, + "seed": 42, + "git_commit": "c33ddcb6dc6ff4b8fcff4f5073aa78b6518eef7c", + "wall_time_seconds": 3488.513890028, + "eval_summary": { + "eval_mean_reward": 106.65819999999997, + "eval_std_reward": 28.625535867682913, + "eval_mean_delivered": 116.4, + "eval_mean_spoiled": 137.2, + "eval_n_episodes": 5 + }, + "eval_per_seed": [ + { + "seed": 100, + "total_reward": 148.06600000000003, + "delivered_units": 132.0, + "spoiled_units": 108.0, + "deliveries_count": 43, + "distance": 193 + }, + { + "seed": 101, + "total_reward": 130.09999999999994, + "delivered_units": 136.0, + "spoiled_units": 131.0, + "deliveries_count": 40, + "distance": 189 + }, + { + "seed": 102, + "total_reward": 77.44219999999997, + "delivered_units": 102.0, + "spoiled_units": 178.0, + "deliveries_count": 42, + "distance": 181 + }, + { + "seed": 103, + "total_reward": 75.71639999999995, + "delivered_units": 96.0, + "spoiled_units": 150.0, + "deliveries_count": 38, + "distance": 189 + }, + { + "seed": 104, + "total_reward": 101.9664, + "delivered_units": 116.0, + "spoiled_units": 119.0, + "deliveries_count": 28, + "distance": 169 + } + ], + "config_raw": { + "run": { + "run_id": "dqn_v5_masked", + "agent": "dqn", + "scenario": "weekday", + "num_episodes": 1500, + "seed": 42, + "output_dir": "experiments", + "description": "v5: DQN with full training-time action masking.\n\nDiagnosis of v4: pickup reward alone didn't break the flat-Q problem.\nInference-time masking on v4 (no retraining) lifted delivered from 75\nto 85, proving the constraint mattered. But Q-values stayed flat\n(spread 1.16) because training itself was polluted: the agent spent\nmany episodes taking actions the env would convert to idle.\n\nv5 fixes:\n- Action mask consulted during action selection (already in v4 at\n inference; now also at training-time exploration)\n- Bellman target uses max_a' Q(s', a') taken only over a' valid in s'\n- Replay buffer stores next_state mask alongside each transition\n\nHyperparameters unchanged from v4 to make the comparison clean \u2014\nthe only variable is masking. Any improvement is attributable to it.\n" + }, + "agent_params": { + "hidden_sizes": [ + 128, + 128 + ], + "learning_rate": 0.0005, + "discount": 0.95, + "epsilon_start": 1.0, + "epsilon_end": 0.1, + "epsilon_decay_episodes": 1200, + "replay_buffer_size": 100000, + "batch_size": 64, + "min_replay_to_train": 1000, + "target_update_interval": 500, + "grad_clip": 1.0, + "device": "auto" + }, + "reward_weights": { + "delivery": 1.0, + "pickup": 0.2, + "spoilage": 0.5, + "distance": 0.01, + "unmet_demand": 0.1, + "priority_bonus": 0.05, + "oversupply_penalty": 0.03 + }, + "eval": { + "n_episodes": 5, + "eval_seeds": [ + 100, + 101, + 102, + 103, + 104 + ] + } + } +} \ No newline at end of file diff --git a/index.html b/index.html index 1b3a7c7..79abc7e 100644 --- a/index.html +++ b/index.html @@ -582,7 +582,7 @@ @@ -1421,6 +1421,11 @@ S = initState("weekday"); draw(); updatePanel(); + if (document.getElementById("policy-select").value === "api-dqn") { + document.getElementById("tb-api-pill").style.display = "flex"; + setApiStatus("checking…", ""); + checkApiHealth(); + } diff --git a/scripts/eval_dqn_in_python.py b/scripts/eval_dqn_in_python.py new file mode 100644 index 0000000..f6b10dd --- /dev/null +++ b/scripts/eval_dqn_in_python.py @@ -0,0 +1,155 @@ +""" +Sanity-check a trained DQN by running it in the Python env (the env it was +trained on). Strips away any JS-side observation building / wiring concerns. + +If this prints decent delivered/spoiled numbers, the model is fine and the +browser demo's worse behavior is due to JS observation mismatch. + +If this prints garbage, the model itself never learned well. + +Usage: + python scripts/eval_dqn_in_python.py + python scripts/eval_dqn_in_python.py --policy experiments/policies/dqn_v1.pt + python scripts/eval_dqn_in_python.py --verbose +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import numpy as np +import torch + +# Make project root importable when run from scripts/ +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +from api.policy_loader import load_policy_from_env # noqa: E402 +from sim.environment import FoodRescueEnv # noqa: E402 + + +def evaluate_one_seed(policy, env: FoodRescueEnv, seed: int, verbose: bool = False) -> dict: + """Run one episode greedily under the policy, return summary metrics.""" + obs, _ = env.reset(seed=seed) + total_reward = 0.0 + action_counts = {"donor": 0, "shelter": 0, "idle": 0} + n_donors = env.num_donors + n_shelters = env.num_shelters + q_spreads = [] + + # Detect which device the policy's weights live on (cpu / mps / cuda). + device = next(policy.q_net.parameters()).device + + for t in range(env.max_episode_steps): + # Use the agent's official select_action so we match training-eval behavior + # exactly (including random tie-breaking, eval mode handling, etc). + action = policy.select_action(env, obs) + + # Also pull the raw Q-values for the diagnostic (q-spread is what told us + # the model was flat). + with torch.no_grad(): + obs_t = torch.from_numpy(obs).float().unsqueeze(0).to(device) + q = policy.q_net(obs_t).squeeze(0).cpu().numpy() + q_spreads.append(float(q.max() - q.min())) + + # Classify what kind of action this was + if action < n_donors: + action_counts["donor"] += 1 + elif action < n_donors + n_shelters: + action_counts["shelter"] += 1 + else: + action_counts["idle"] += 1 + + if verbose and t < 5: + v = env.vehicles[env.current_vehicle_idx] + # Try common attribute names for vehicle id, fall back to index. + v_label = ( + getattr(v, "id", None) + or getattr(v, "vehicle_id", None) + or f"V{env.current_vehicle_idx}" + ) + try: + load = v.current_load() + except Exception: + load = getattr(v, "load", "?") + print(f" step {t}: vehicle={v_label} load={load} " + f"action={action} q-spread={q.max()-q.min():.2f} " + f"q-top3={[round(x, 2) for x in sorted(q.tolist(), reverse=True)[:3]]}") + + obs, reward, term, trunc, _ = env.step(action) + total_reward += reward + if term or trunc: + break + + em = env._episode_metrics + return { + "seed": seed, + "total_reward": round(total_reward, 2), + "delivered_units": round(em["total_delivered_units"], 1), + "spoiled_units": round(em["total_spoiled_units"], 1), + "deliveries_count": em["deliveries_count"], + "action_counts": action_counts, + "mean_q_spread": round(float(np.mean(q_spreads)), 2), + } + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument( + "--policy", + default="experiments/policies/dqn_v3_normalized.pt", + help="Path to the .pt policy file", + ) + ap.add_argument("--seeds", nargs="+", type=int, default=[100, 101, 102, 103, 104]) + ap.add_argument("--verbose", action="store_true") + args = ap.parse_args() + + import os + print(f"Loading policy from: {args.policy}") + # The public loader reads FOOD_RESCUE_MODEL_PATH from env — set it from CLI arg. + os.environ["FOOD_RESCUE_MODEL_PATH"] = args.policy + # Make sure we're not also pointed at the registry; local path wins. + os.environ.pop("FOOD_RESCUE_MODEL_NAME", None) + policy, info = load_policy_from_env() + if hasattr(policy, "set_training"): + policy.set_training(False) + print(f" Model: {info.get('agent_kind')} | obs_dim={info.get('obs_dim')} " + f"| num_actions={info.get('num_actions')}") + print() + + env = FoodRescueEnv() + print(f"Env: {env.num_donors} donors, {env.num_shelters} shelters, " + f"{env.max_episode_steps} steps/episode") + print() + + results = [] + for seed in args.seeds: + if args.verbose: + print(f"--- seed {seed} (first 5 steps) ---") + r = evaluate_one_seed(policy, env, seed, verbose=args.verbose) + results.append(r) + print(f"seed={seed}: reward={r['total_reward']:+.1f} " + f"delivered={r['delivered_units']:.0f}u " + f"spoiled={r['spoiled_units']:.0f}u " + f"deliveries={r['deliveries_count']} " + f"q-spread-avg={r['mean_q_spread']:.2f} " + f"actions={r['action_counts']}") + + # Aggregate + rewards = [r["total_reward"] for r in results] + deliv = [r["delivered_units"] for r in results] + spoil = [r["spoiled_units"] for r in results] + spreads = [r["mean_q_spread"] for r in results] + + print() + print("=" * 60) + print(f"AGGREGATE over {len(args.seeds)} seeds:") + print(f" Reward: {np.mean(rewards):+.2f} ± {np.std(rewards):.2f}") + print(f" Delivered: {np.mean(deliv):.1f} ± {np.std(deliv):.1f} units") + print(f" Spoiled: {np.mean(spoil):.1f} ± {np.std(spoil):.1f} units") + print(f" Q-spread: {np.mean(spreads):.2f} (avg max-min Q-value per step)") + print("=" * 60) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scripts/register_models.py b/scripts/register_models.py index c77667e..1851eb4 100644 --- a/scripts/register_models.py +++ b/scripts/register_models.py @@ -26,6 +26,14 @@ ("sarsa_v1", "food_rescue_sarsa", "SARSA on-policy baseline"), ("dqn_v1", "food_rescue_dqn", "DQN baseline on weekday scenario"), ("dqn_v2_holiday", "food_rescue_dqn", "DQN on holiday_rush scenario"), + ("dqn_v3_normalized", "food_rescue_dqn", + "DQN with normalized reward scale (delivery=1) and gamma=0.99. " + "Eval: -24 reward, 79 delivered, 217 spoiled."), + ("dqn_v4_dense", "food_rescue_dqn", + "DQN with normalized reward + pickup shaping (0.2/unit) and gamma=0.95. " + "Eval: +13.6 reward, 74.6 delivered, 197 spoiled. Diagnosed: flat Q-values."), + ("dqn_v5_masked", "food_rescue_dqn", + "Eval: +550 reward, 116 delivered, 137 spoiled."), ] @@ -64,6 +72,12 @@ def register_run(client: MlflowClient, run, model_name: str, description: str) - run_id=run.info.run_id, description=description, ) + client.transition_model_version_stage( + name=model_name, + version=version.version, + stage="Production", + archive_existing_versions=True, + ) print(f" Registered: {model_name} version {version.version} " f"(run: {run.data.tags.get('mlflow.runName')})") diff --git a/sim/dqn_v4_dense.yaml b/sim/dqn_v4_dense.yaml new file mode 100644 index 0000000..855c4c5 --- /dev/null +++ b/sim/dqn_v4_dense.yaml @@ -0,0 +1,59 @@ +run: + run_id: dqn_v4_dense + agent: dqn + scenario: weekday + num_episodes: 1500 + seed: 42 + output_dir: experiments + description: | + v4: dense pickup signal + γ back to 0.95 + higher ε floor. + + Diagnosis of v3 (see scripts/eval_dqn_in_python.py output): + - Q-values collapsed to a flat ~18-20 across all 11 actions + - Average q-spread per step: 1.19 (healthy is 5-15) + - Step 0 Q-values identical across 5 different random seeds + - Model treated every action as equally good; survived only by + env forgiveness and random tie-breaking + - Root cause: γ=0.99 with sparse delivery-only reward smoothed + value across all states; the network learned ONE mean value + + v4 fixes: + - Add pickup reward (dense intermediate signal at 0.2 per unit + loaded). Breaks the value-smoothing symmetry — picking up at a + donor with food is now a learnable positive event, not just a + precursor to delivery. + - γ=0.95 (was 0.99). Preserves discrimination between near-term + and far-term consequences. With 200-step episodes, γ=0.95 + values reward 14 steps out at ~50% — appropriate horizon. + - ε_end=0.10 (was 0.02). Keep exploring throughout training; + model needs to discover non-greedy good actions. + - min_replay_to_train=1000 (was 5000). Start learning sooner. + +agent_params: + hidden_sizes: [128, 128] + learning_rate: 0.0005 + discount: 0.95 + epsilon_start: 1.0 + epsilon_end: 0.10 + epsilon_decay_episodes: 1200 + replay_buffer_size: 100000 + batch_size: 64 + min_replay_to_train: 1000 + target_update_interval: 500 + grad_clip: 1.0 + device: auto + +# Normalized scale (delivery=1, not 10) keeps DQN TD targets bounded. +# New pickup reward gives the dense intermediate signal v3 was missing. +reward_weights: + delivery: 1.0 + pickup: 0.2 + spoilage: 0.5 + distance: 0.01 + unmet_demand: 0.1 + priority_bonus: 0.05 + oversupply_penalty: 0.03 + +eval: + n_episodes: 5 + eval_seeds: [100, 101, 102, 103, 104] diff --git a/sim/environment.py b/sim/environment.py index 9a0e5ae..3b73fb9 100644 --- a/sim/environment.py +++ b/sim/environment.py @@ -62,9 +62,9 @@ class RewardWeights: spoilage: float = 5.0 distance: float = 0.1 unmet_demand: float = 1.0 - priority_bonus: float = 0.5 # extra reward for delivering to priority-1 shelters - oversupply_penalty: float = 0.3 # small penalty for delivering more than needed - + priority_bonus: float = 0.5 + oversupply_penalty: float = 0.3 + pickup: float = 0.0 # NEW: dense intermediate signal for loading food at donor @dataclass class EnvConfig: @@ -173,6 +173,44 @@ def _build_observation_space(self) -> spaces.Box: shape=(n_features,), dtype=np.float32, ) + def action_mask(self) -> np.ndarray: + """ + Boolean mask of valid actions for the CURRENT vehicle. + + Returns a (num_actions,) bool array where True = legal action. + Used both during training (mask infeasible Q-values before argmax) + and at inference time (DQN's select_action consults it). + + Mask rules: + - "head to donor i" is valid iff donor i has pending food AND + the current vehicle isn't already at full capacity + - "head to shelter j" is valid iff shelter j has unmet demand AND + the current vehicle has at least one unit loaded + - "idle" is always valid (so we never mask everything out) + """ + v = self.vehicles[self.current_vehicle_idx] + cap = v.capacity if v.capacity > 0 else 20 + has_food = v.current_load() > 0 + has_space = v.current_load() < cap + + mask = np.zeros(self.action_space.n, dtype=bool) + + # Donor actions: 0 .. num_donors-1 + for i, donor in enumerate(self.scenario.donors): + if has_space and donor.total_pending_quantity() > 0: + mask[i] = True + + # Shelter actions: num_donors .. num_donors+num_shelters-1 + for j, shelter in enumerate(self.scenario.shelters): + if has_food and shelter.current_demand > 0: + mask[self.num_donors + j] = True + + # Idle is always available as a fallback + mask[self.num_donors + self.num_shelters] = True + + return mask + + # ----------------------------- # Action decoding @@ -368,6 +406,15 @@ def step(self, action: int): if donor is not None: picked = donor.pickup_all(self.current_step) leftover = v.load_batches(picked) + # Track pickup volume for reward shaping. + # picked_units = total units the vehicle actually loaded + # (the difference between picked-up batches and leftover). + picked_total = sum(b.quantity for b in picked) + leftover_total = sum(b.quantity for b in leftover) + step_metrics["picked_units"] = ( + step_metrics.get("picked_units", 0.0) + + (picked_total - leftover_total) + ) # Leftovers go back to donor (vehicle was full) donor.pending_batches.extend(leftover) v.clear_target() @@ -472,6 +519,7 @@ def _compute_reward(self, m: dict) -> float: w = self.config.reward_weights delivered = m["delivered_units"] + picked = m.get("picked_units", 0.0) # NEW wasted = m["wasted_units"] spoiled = m["spoiled_units_donor"] + m["spoiled_units_vehicle"] distance = m["distance_traveled"] @@ -481,13 +529,14 @@ def _compute_reward(self, m: dict) -> float: unmet = sum(s.current_demand for s in self.scenario.shelters) reward = ( - w.delivery * delivered - + w.priority_bonus * priority * delivered # bonus scaled by units delivered - - w.oversupply_penalty * wasted - - w.spoilage * spoiled - - w.distance * distance - - w.unmet_demand * (unmet / 500.0) # softer signal; was /100, dominated reward - ) + w.delivery * delivered + + w.pickup * picked # NEW + + w.priority_bonus * priority * delivered + - w.oversupply_penalty * wasted + - w.spoilage * spoiled + - w.distance * distance + - w.unmet_demand * (unmet / 500.0) + ) return float(reward) def _update_episode_metrics(self, m: dict) -> None: diff --git a/tests/test_dqn.py b/tests/test_dqn.py index 27c7899..3902dbb 100644 --- a/tests/test_dqn.py +++ b/tests/test_dqn.py @@ -30,28 +30,29 @@ def test_different_inputs_different_outputs(self): class TestReplayBuffer: def test_push_and_size(self): - buf = ReplayBuffer(capacity=100) + buf = ReplayBuffer(capacity=100, num_actions=11) for i in range(10): buf.push(np.zeros(31), 0, 1.0, np.zeros(31), False) assert len(buf) == 10 def test_capacity_overflow(self): - buf = ReplayBuffer(capacity=10) + buf = ReplayBuffer(capacity=10, num_actions=11, seed=0) for i in range(20): buf.push(np.zeros(31), 0, 1.0, np.zeros(31), False) assert len(buf) == 10 # caps at capacity def test_sample_shapes(self): - buf = ReplayBuffer(capacity=100, seed=0) + buf = ReplayBuffer(capacity=100, num_actions=11, seed=0) for _ in range(50): buf.push(np.random.randn(31), np.random.randint(11), 1.0, np.random.randn(31), False) - obs, act, rew, next_obs, done = buf.sample(16) + obs, act, rew, next_obs, done, next_mask = buf.sample(16) assert obs.shape == (16, 31) assert act.shape == (16,) assert rew.shape == (16,) assert next_obs.shape == (16, 31) assert done.shape == (16,) + assert next_mask.shape == (16, 11) class TestDQNAgent: diff --git a/train.py b/train.py index 9f37808..b2d0ef7 100644 --- a/train.py +++ b/train.py @@ -231,7 +231,12 @@ def train_dqn(env: FoodRescueEnv, agent, num_episodes: int, seed: int) -> list[d total_reward += reward steps += 1 - agent.store_transition(obs, action, reward, next_obs, float(done)) + # Capture the mask AFTER the step has advanced env state, so it + # reflects what's legal from the NEXT state. Falls back to all-True + # if the env doesn't support masking. + next_mask = env.action_mask() if hasattr(env, "action_mask") else None + agent.store_transition(obs, action, reward, next_obs, float(done), + next_mask=next_mask) loss = agent.train_step() if loss is not None: ep_losses.append(loss)