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:param policy: Policy instance associated with this AgentProcessor. |
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:param max_trajectory_length: Maximum length of a trajectory before it is added to the trainer. |
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""" |
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self.experience_buffers: Dict[str, List] = defaultdict(list) |
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self.experience_buffers: Dict[str, List[AgentExperience]] = defaultdict(list) |
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self.stats: Dict[str, List] = defaultdict(list) |
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self.stats: Dict[str, List[float]] = defaultdict(list) |
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# Note: this is needed until we switch to AgentExperiences as the data input type. |
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# We still need some info from the policy (memories, previous actions) |
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# that really should be gathered by the env-manager. |
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self.trainer = trainer |
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def __str__(self): |
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return "local_buffers :\n{0}".format( |
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"\n".join( |
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[ |
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"\tagent {0} :{1}".format(k, str(self.experience_buffers[k])) |
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for k in self.experience_buffers.keys() |
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] |
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) |
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) |
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def add_experiences( |
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self, |
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curr_info: BrainInfo, |
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self.last_take_action_outputs[agent_id] = take_action_outputs |
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# Store the environment reward |
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tmp_environment = np.array(next_info.rewards, dtype=np.float32) |
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tmp_environment_reward = np.array(next_info.rewards, dtype=np.float32) |
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for agent_id in next_info.agents: |
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stored_info = self.last_brain_info.get(agent_id, None) |
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values = stored_take_action_outputs["value_heads"] |
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experience = AgentExperience( |
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obs=obs, |
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reward=tmp_environment[next_idx], |
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reward=tmp_environment_reward[next_idx], |
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done=done, |
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action=action, |
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action_probs=action_probs, |
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Ervin Teng