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change method interface: AgentInterface.emulate_act_on_trainer(transition: Transition) -> emulate_act_on_trainer(action: ActionType)

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zach dwiel
2019-04-05 10:53:03 -04:00
committed by Zach Dwiel
parent b20e795ce0
commit f2fead57e5
3 changed files with 8 additions and 9 deletions
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6
rl_coach/agents/agent.py
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@@ -813,6 +813,7 @@ class Agent(AgentInterface):
curr_state = self.curr_state
self.last_action_info = self.choose_action(curr_state)
# is it intentional that self.last_action_info is not filtered?
filtered_action_info = self.output_filter.filter(self.last_action_info)
return filtered_action_info
@@ -1037,7 +1038,7 @@ class Agent(AgentInterface):
# TODO-remove - this is a temporary flow, used by the trainer worker, duplicated from observe() - need to create
# an external trainer flow reusing the existing flow and methods [e.g. observe(), step(), act()]
def emulate_act_on_trainer(self, transition: Transition) -> ActionInfo:
def emulate_act_on_trainer(self, action: ActionType) -> ActionInfo:
"""
This emulates the act using the transition obtained from the rollout worker on the training worker
in case of distributed training.
@@ -1053,7 +1054,8 @@ class Agent(AgentInterface):
self.total_steps_counter += 1
self.current_episode_steps_counter += 1
self.last_action_info = transition.action
# these types don't match: ActionInfo = ActionType
self.last_action_info = action
return self.last_action_info

9
rl_coach/agents/agent_interface.py
View File

@@ -144,14 +144,11 @@ class AgentInterface(object):
# TODO-remove - this is a temporary flow, used by the trainer worker, duplicated from observe() - need to create
# an external trainer flow reusing the existing flow and methods [e.g. observe(), step(), act()]
def emulate_act_on_trainer(self, transition: Transition) -> ActionInfo:
def emulate_act_on_trainer(self, action: ActionType) -> ActionInfo:
"""
This emulates the act using the transition obtained from the rollout worker on the training worker
in case of distributed training.
Get a decision of the next action to take.
The action is dependent on the current state which the agent holds from resetting the environment or from
the observe function.
:return: A tuple containing the actual action and additional info on the action
:return: A tuple containing the actual action
"""
raise NotImplementedError("")
@@ -173,7 +170,7 @@ class AgentInterface(object):
:return: None
"""
raise NotImplementedError("")
def run_off_policy_evaluation(self) -> None:
"""
Run off-policy evaluation estimators to evaluate the trained policy performance against a dataset.

2
rl_coach/level_manager.py
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@@ -313,7 +313,7 @@ class LevelManager(EnvironmentInterface):
# for i in range(self.steps_limit.num_steps):
# let the agent observe the result and decide if it wants to terminate the episode
done = acting_agent.emulate_observe_on_trainer(transition)
acting_agent.emulate_act_on_trainer(transition)
acting_agent.emulate_act_on_trainer(transition.action)
if done:
self.handle_episode_ended()