fix(nyz): fix mock and config bugs

ding/entry/tests/test_serial_entry_preference_based_irl_onpolicy.py

@@ -17,6 +17,8 @@
 def test_serial_pipeline_trex_onpolicy():
     exp_name = 'trex_onpolicy_test_serial_pipeline_trex_onpolicy_expert'
     config = [deepcopy(cartpole_ppo_config), deepcopy(cartpole_ppo_create_config)]
+    config[0].policy.learn.learner = dict()
+    config[0].policy.learn.learner.hook = dict()
     config[0].policy.learn.learner.hook.save_ckpt_after_iter = 100
     config[0].exp_name = exp_name
     expert_policy = serial_pipeline_onpolicy(config, seed=0)

ding/framework/middleware/collector.py

@@ -101,7 +101,7 @@ def __call__(self, ctx: "OnlineRLContext") -> None:
         """
         Overview:
             An encapsulation of inference and rollout middleware. Stop when completing \
-                the target number of steps.
+            the target number of steps.
         Input of ctx:
             - env_step (:obj:`int`): The env steps which will increase during collection.
         """
@@ -143,7 +143,7 @@ class EpisodeCollector:
     """
     Overview:
         The class of the collector running by episodes, including model inference and transition \
-            process. Use the `__call__` method to execute the whole collection process.
+        process. Use the `__call__` method to execute the whole collection process.
     """
 
     def __init__(self, cfg: EasyDict, policy, env: BaseEnvManager, random_collect_size: int = 0) -> None:
@@ -168,7 +168,7 @@ def __call__(self, ctx: "OnlineRLContext") -> None:
         """
         Overview:
             An encapsulation of inference and rollout middleware. Stop when completing the \
-                target number of episodes.
+            target number of episodes.
         Input of ctx:
             - env_episode (:obj:`int`): The env env_episode which will increase during collection.
         """

ding/framework/middleware/tests/mock_for_test.py

@@ -63,7 +63,7 @@ def process_transition(self, obs: Any, model_output: dict, timestep: namedtuple)
             'logit': 1.0,
             'value': 2.0,
             'reward': 0.1,
-            'done': True,
+            'done': timestep.done,
         }
         return transition
 
@@ -75,7 +75,6 @@ def __init__(self) -> None:
         self.env_num = env_num
         self.obs_dim = obs_dim
         self.closed = False
-        self._reward_grow_indicator = 1
         self._steps = [0 for _ in range(self.env_num)]
 
     @property
@@ -111,11 +110,10 @@ def step(self, actions: tnp.ndarray) -> List[tnp.ndarray]:
                 obs=torch.rand(self.obs_dim),
                 reward=1.0,
                 done=done,
-                info={'eval_episode_return': self._reward_grow_indicator * 1.0} if done else {},
+                info={'eval_episode_return': 10.0} if done else {},
                 env_id=i,
             )
             timesteps.append(tnp.array(timestep))
-        self._reward_grow_indicator += 1  # eval_episode_return will increase as step method is called
         return timesteps
 
 

ding/framework/middleware/tests/test_evaluator.py

@@ -24,4 +24,4 @@ def test_interaction_evaluator():
                 # there are 2 env_num and 5 episodes in the test.
                 # so when interaction_evaluator runs the first time, reward is [[1, 2, 3], [2, 3]] and the avg = 2.2
                 # the second time, reward is [[4, 5, 6], [5, 6]] . . .
-                assert ctx.eval_value == 2.2 + i // 10 * 3.0
+                assert ctx.eval_value == 10.0