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speedup accessing actions one agent at a time #4261

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speedup accessing actions one agent at a time #4261

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2 changes: 1 addition & 1 deletion ml-agents/mlagents/trainers/agent_processor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ def _process_step(
action_pre = None
action_probs = stored_take_action_outputs["log_probs"][idx]
action_mask = stored_decision_step.action_mask
prev_action = self.policy.retrieve_previous_action([global_id])[0, :]
prev_action = self.policy.retrieve_previous_action_single(global_id)
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Would you be opposed to renaming the method retrieve_previous_action and the old method to retrieve_previous_actions? Seems more descriptive and more similar to the methods in the LL-API.

experience = AgentExperience(
obs=obs,
reward=step.reward,
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10 changes: 10 additions & 0 deletions ml-agents/mlagents/trainers/policy/tf_policy.py
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Expand Up @@ -353,6 +353,16 @@ def retrieve_previous_action(self, agent_ids: List[str]) -> np.ndarray:
action_matrix[index, :] = self.previous_action_dict[agent_id]
return action_matrix

def retrieve_previous_action_single(self, agent_id: str) -> np.ndarray:
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I think this method needs to be moved to policy.py with the other retrieve methods (#4254).

"""
A more efficient version of retrieve_previous_action() for a single
agent at a time.
"""
prev_action = self.previous_action_dict.get(agent_id)
if prev_action is not None:
return prev_action
return np.zeros(self.num_branches, dtype=np.int)

def remove_previous_action(self, agent_ids):
for agent_id in agent_ids:
if agent_id in self.previous_action_dict:
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