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）。
         """
-        obs_data, _ = self.observation_process(env_obs)
-        act_data = self.predict([obs_data])[0]
-        return self.action_process(act_data, is_stochastic=False)
+        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).