[rllib] Add more warnings when multi-agent envs might not be set up right

python/ray/rllib/env/async_vector_env.py

@@ -268,12 +268,17 @@ def send_actions(self, action_dict):
                 raise ValueError("Env {} is already done".format(env_id))
             env = self.envs[env_id]
             obs, rewards, dones, infos = env.step(agent_dict)
+            assert isinstance(obs, dict), "Not a multi-agent obs"
+            assert isinstance(rewards, dict), "Not a multi-agent reward"
+            assert isinstance(dones, dict), "Not a multi-agent return"
+            assert isinstance(infos, dict), "Not a multi-agent info"
             if dones["__all__"]:
                 self.dones.add(env_id)
             self.env_states[env_id].observe(obs, rewards, dones, infos)
 
     def try_reset(self, env_id):
         obs = self.env_states[env_id].reset()
+        assert isinstance(obs, dict), "Not a multi-agent obs"
         if obs is not None and env_id in self.dones:
             self.dones.remove(env_id)
         return obs

python/ray/rllib/evaluation/policy_evaluator.py

@@ -168,6 +168,11 @@ def __init__(self,
         model_config = model_config or {}
         policy_mapping_fn = (policy_mapping_fn
                              or (lambda agent_id: DEFAULT_POLICY_ID))
+        if not callable(policy_mapping_fn):
+            raise ValueError(
+                "Policy mapping function not callable. If you're using Tune, "
+                "make sure to escape the function with tune.function() "
+                "to prevent it from being evaluated as an expression.")
         self.env_creator = env_creator
         self.sample_batch_size = batch_steps * num_envs
         self.batch_mode = batch_mode
@@ -230,7 +235,14 @@ def make_env(vector_index):
             self.policy_map = self._build_policy_map(policy_dict,
                                                      policy_config)
 
-        self.multiagent = self.policy_map.keys() != {DEFAULT_POLICY_ID}
+        self.multiagent = set(self.policy_map.keys()) != {DEFAULT_POLICY_ID}
+        if self.multiagent:
+            if not (isinstance(self.env, MultiAgentEnv)
+                    or isinstance(self.env, AsyncVectorEnv)):
+                raise ValueError(
+                    "Have multiple policy graphs {}, but the env ".format(
+                        self.policy_map) +
+                    "{} is not a subclass of MultiAgentEnv?".format(self.env))
 
         self.filters = {
             policy_id: get_filter(observation_filter,

python/ray/rllib/evaluation/sampler.py

@@ -218,7 +218,7 @@ def _env_runner(async_vector_env,
             horizon = (
                 async_vector_env.get_unwrapped()[0].spec.max_episode_steps)
     except Exception:
-        print("Warning, no horizon specified, assuming infinite")
+        print("*** WARNING ***: no episode horizon specified, assuming inf")
     if not horizon:
         horizon = float("inf")
 

python/ray/tune/suggest/variant_generator.py

@@ -155,6 +155,11 @@ def _resolve_lambda_vars(spec, lambda_vars):
                 value = fn(_UnresolvedAccessGuard(spec))
             except RecursiveDependencyError as e:
                 error = e
+            except Exception:
+                raise ValueError(
+                    "Failed to evaluate expression: {}: {}".format(path, fn) +
+                    ". If you meant to pass this as a function literal, use "
+                    "tune.function() to escape it.")
             else:
                 _assign_value(spec, path, value)
                 resolved[path] = value