microsoft
reinforcement learning example give errors.
I run reinforcement learning examples\rl\simple_example.ipynb and get a error at Training workflow section. here is the error. how to resolve it?
AssertionError Traceback (most recent call last) Cell In[7], line 24 18 vessel_kwargs = { 19 "update_kwargs": {"batch_size": 16, "repeat": 5}, 20 "episode_per_iter": 50, 21 } 23 print("Training started") ---> 24 train( 25 simulator_fn=lambda position: SimpleSimulator(position, NSTEPS), 26 state_interpreter=state_interpreter, 27 action_interpreter=action_interpreter, 28 policy=policy, 29 reward=reward, 30 initial_states=cast(List[float], SimpleDataset([10.0, 50.0, 100.0])), 31 trainer_kwargs=trainer_kwargs, 32 vessel_kwargs=vessel_kwargs, 33 ) 34 print("Training finished")
File e:\anaconda3\envs\qlib230510\lib\site-packages\qlib\rl\trainer\api.py:63, in train(simulator_fn, state_interpreter, action_interpreter, initial_states, policy, reward, vessel_kwargs, trainer_kwargs) 53 vessel = TrainingVessel( 54 simulator_fn=simulator_fn, 55 state_interpreter=state_interpreter, (...) 60 **vessel_kwargs, 61 ) 62 trainer = Trainer(**trainer_kwargs) ---> 63 trainer.fit(vessel)
File e:\anaconda3\envs\qlib230510\lib\site-packages\qlib\rl\trainer\trainer.py:224, in Trainer.fit(self, vessel, ckpt_path) 222 with _wrap_context(vessel.train_seed_iterator()) as iterator: 223 vector_env = self.venv_from_iterator(iterator) --> 224 self.vessel.train(vector_env) 225 del vector_env # FIXME: Explicitly delete this object to avoid memory leak. 227 self._call_callback_hooks("on_train_end")
File e:\anaconda3\envs\qlib230510\lib\site-packages\qlib\rl\trainer\vessel.py:171, in TrainingVessel.train(self, vector_env) 168 self.policy.train() 170 with vector_env.collector_guard(): --> 171 collector = Collector(self.policy, vector_env, VectorReplayBuffer(self.buffer_size, len(vector_env))) 173 # Number of episodes collected in each training iteration can be overridden by fast dev run. 174 if self.trainer.fast_dev_run is not None:
File e:\anaconda3\envs\qlib230510\lib\site-packages\tianshou\data\collector.py:79, in Collector.init(self, policy, env, buffer, preprocess_fn, exploration_noise) 77 self._action_space = self.env.action_space 78 # avoid creating attribute outside init ---> 79 self.reset(False)
File e:\anaconda3\envs\qlib230510\lib\site-packages\tianshou\data\collector.py:130, in Collector.reset(self, reset_buffer, gym_reset_kwargs) 117 # use empty Batch for "state" so that self.data supports slicing 118 # convert empty Batch to None when passing data to policy 119 self.data = Batch( 120 obs={}, 121 act={}, (...) 128 policy={} 129 ) --> 130 self.reset_env(gym_reset_kwargs) 131 if reset_buffer: 132 self.reset_buffer()
File e:\anaconda3\envs\qlib230510\lib\site-packages\tianshou\data\collector.py:146, in Collector.reset_env(self, gym_reset_kwargs) 144 """Reset all of the environments.""" 145 gym_reset_kwargs = gym_reset_kwargs if gym_reset_kwargs else {} --> 146 obs, info = self.env.reset(**gym_reset_kwargs) 147 if self.preprocess_fn: 148 processed_data = self.preprocess_fn( 149 obs=obs, info=info, env_id=np.arange(self.env_num) 150 )
File e:\anaconda3\envs\qlib230510\lib\site-packages\qlib\rl\utils\finite_env.py:233, in FiniteVectorEnv.reset(self, id) 231 id2idx = {i: k for k, i in enumerate(wrapped_id)} 232 if request_id: --> 233 for i, o in zip(request_id, super().reset(request_id)): 234 obs[id2idx[i]] = self._postproc_env_obs(o) 236 for i, o in zip(wrapped_id, obs):
File e:\anaconda3\envs\qlib230510\lib\site-packages\tianshou\env\venvs.py:280, in BaseVectorEnv.reset(self, id, **kwargs) 277 self.workers[i].send(None, **kwargs) 278 ret_list = [self.workers[i].recv() for i in id] --> 280 assert ( 281 isinstance(ret_list[0], (tuple, list)) and len(ret_list[0]) == 2 282 and isinstance(ret_list[0][1], dict) 283 ) 285 obs_list = [r[0] for r in ret_list] 287 if isinstance(obs_list[0], tuple): # type: ignore
AssertionError:
