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fix for finding the right filter checkpoint to restore + do not update internal filter state when evaluating + fix SharedRunningStats checkpoint filenames (#147)

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Gal Leibovich
2018-12-17 21:36:27 +02:00
committed by GitHub
parent b4bc8a476c
commit 4c914c057c
4 changed files with 19 additions and 15 deletions
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10
rl_coach/agents/agent.py
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@@ -762,7 +762,8 @@ class Agent(AgentInterface):
# informed action
if self.pre_network_filter is not None:
# before choosing an action, first use the pre_network_filter to filter out the current state
curr_state = self.run_pre_network_filter_for_inference(self.curr_state)
update_filter_internal_state = self.phase is not RunPhase.TEST
curr_state = self.run_pre_network_filter_for_inference(self.curr_state, update_filter_internal_state)
else:
curr_state = self.curr_state
@@ -772,15 +773,18 @@ class Agent(AgentInterface):
return filtered_action_info
def run_pre_network_filter_for_inference(self, state: StateType) -> StateType:
def run_pre_network_filter_for_inference(self, state: StateType, update_filter_internal_state: bool=True)\
-> StateType:
"""
Run filters which where defined for being applied right before using the state for inference.
:param state: The state to run the filters on
:param update_filter_internal_state: Should update the filter's internal state - should not update when evaluating
:return: The filtered state
"""
dummy_env_response = EnvResponse(next_state=state, reward=0, game_over=False)
return self.pre_network_filter.filter(dummy_env_response)[0].next_state
return self.pre_network_filter.filter(dummy_env_response,
update_internal_state=update_filter_internal_state)[0].next_state
def get_state_embedding(self, state: dict) -> np.ndarray:
"""

2
rl_coach/agents/clipped_ppo_agent.py
View File

@@ -325,7 +325,7 @@ class ClippedPPOAgent(ActorCriticAgent):
self.update_log()
return None
def run_pre_network_filter_for_inference(self, state: StateType):
def run_pre_network_filter_for_inference(self, state: StateType, update_internal_state: bool=False):
dummy_env_response = EnvResponse(next_state=state, reward=0, game_over=False)
return self.pre_network_filter.filter(dummy_env_response, update_internal_state=False)[0].next_state