修复PPO评估推理返回None异常

agent_ppo/agent.py

@@ -108,9 +108,17 @@ class Agent(BaseAgent):
 
         评估时推理（贪心）。
         """
+        try:
             obs_data, _ = self.observation_process(env_obs)
             act_data = self.predict([obs_data])[0]
             return self.action_process(act_data, is_stochastic=False)
+        except Exception as err:
+            if self.logger:
+                if hasattr(self.logger, "exception"):
+                    self.logger.exception(f"exploit fallback action due to inference error: {err}")
+                else:
+                    self.logger.error(f"exploit fallback action due to inference error: {err}")
+            return self._fallback_action(env_obs)
 
     def learn(self, list_sample_data):
         """Delegate to Algorithm for PPO update.
@@ -146,6 +154,24 @@ class Agent(BaseAgent):
         if self.logger:
             self.logger.info(f"load model {model_file_path} successfully")
 
+    def _fallback_action(self, env_obs):
+        """Return a valid action instead of None during evaluation failures."""
+        legal_action = [1] * Config.ACTION_NUM
+        if isinstance(env_obs, dict):
+            observation = env_obs.get("observation")
+            if isinstance(observation, dict):
+                raw_legal = observation.get("legal_action") or observation.get("legal_act")
+                if isinstance(raw_legal, (list, tuple)) and raw_legal:
+                    legal_action = [int(x) for x in raw_legal[: Config.ACTION_NUM]]
+        if len(legal_action) < Config.ACTION_NUM:
+            legal_action.extend([0] * (Config.ACTION_NUM - len(legal_action)))
+        for action, is_legal in enumerate(legal_action):
+            if is_legal > 0:
+                self.last_action = action
+                return action
+        self.last_action = 0
+        return 0
+
     def _run_model(self, feature):
         """Gradient-free forward pass, returns (logits_np, value_np).
 

agent_ppo/feature/preprocessor.py

@@ -31,6 +31,11 @@ def _signed_norm(v, v_max):
     return float(np.clip(float(v) / float(v_max), -1.0, 1.0))
 
 
+def _as_dict(value):
+    """Return a dict for optional nested observation fields."""
+    return value if isinstance(value, dict) else {}
+
+
 class Preprocessor:
     """Feature preprocessor for Robot Vacuum.
 
@@ -131,13 +136,16 @@ class Preprocessor:
 
         从 env_obs 字典中提取并缓存所有需要的状态量。
         """
-        observation = env_obs["observation"]
+        env_obs = _as_dict(env_obs)
-        frame_state = observation.get("frame_state", {})
+        observation = _as_dict(env_obs.get("observation"))
-        extra_frame_state = env_obs.get("extra_info", {}).get("frame_state", {})
+        frame_state = _as_dict(observation.get("frame_state"))
-        env_info = observation.get("env_info", {})
+        extra_info = _as_dict(env_obs.get("extra_info"))
-        hero = frame_state.get("heroes", {})
+        extra_frame_state = _as_dict(extra_info.get("frame_state"))
+        env_info = _as_dict(observation.get("env_info"))
+        hero = frame_state.get("heroes") or {}
         if isinstance(hero, list):
             hero = hero[0] if hero else {}
+        hero = _as_dict(hero)
 
         self.last_action = int(last_action)
         self.step_no = int(observation.get("step_no", env_info.get("step_no", self.step_no)))
@@ -149,7 +157,7 @@ class Preprocessor:
         self.prev_low_battery = self.low_battery
         self.was_recharge_mode = self.recharge_mode
         self.prev_pos = self.cur_pos if self.has_position_history else None
-        hero_pos = hero.get("pos") or env_info.get("pos") or {"x": self.cur_pos[0], "z": self.cur_pos[1]}
+        hero_pos = _as_dict(hero.get("pos") or env_info.get("pos") or {"x": self.cur_pos[0], "z": self.cur_pos[1]})
         self.cur_pos = (int(hero_pos.get("x", self.cur_pos[0])), int(hero_pos.get("z", self.cur_pos[1])))
         self.has_position_history = True
 
@@ -198,7 +206,8 @@ class Preprocessor:
 
         organs = frame_state.get("organs") or extra_frame_state.get("organs") or []
         npcs = frame_state.get("npcs") or extra_frame_state.get("npcs") or []
-        self.npcs = list(npcs)
+        organs = organs if isinstance(organs, (list, tuple)) else []
+        self.npcs = list(npcs) if isinstance(npcs, (list, tuple)) else []
         self._update_charger_state(hx, hz, organs)
         self._update_npc_state(hx, hz, self.npcs)
         self._update_recharge_mode()

agent_ppo/workflow/train_workflow.py

@@ -132,8 +132,12 @@ class EpisodeRunner:
                 final_reward = 0.0
                 if done:
                     fm = self.agent.preprocessor
-                    env_info = env_obs["observation"]["env_info"]
+                    observation = env_obs.get("observation") or {}
-                    extra_info = env_obs.get("extra_info", {})
+                    observation = observation if isinstance(observation, dict) else {}
+                    env_info = observation.get("env_info") or {}
+                    env_info = env_info if isinstance(env_info, dict) else {}
+                    extra_info = env_obs.get("extra_info") or {}
+                    extra_info = extra_info if isinstance(extra_info, dict) else {}
                     total_score = env_info.get("total_score", fm.total_score)
                     remaining_charge = env_info.get("remaining_charge", fm.remaining_charge)
                     charge_count = env_info.get("charge_count", fm.charge_count)