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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 curr_state = self.curr_state
self.last_action_info = self.choose_action(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) filtered_action_info = self.output_filter.filter(self.last_action_info)
return filtered_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 # 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()] # 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 This emulates the act using the transition obtained from the rollout worker on the training worker
in case of distributed training. in case of distributed training.
@@ -1053,7 +1054,8 @@ class Agent(AgentInterface):
self.total_steps_counter += 1 self.total_steps_counter += 1
self.current_episode_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 return self.last_action_info

7
rl_coach/agents/agent_interface.py
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@@ -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 # 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()] # 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 This emulates the act using the transition obtained from the rollout worker on the training worker
in case of distributed training. in case of distributed training.
Get a decision of the next action to take. :return: A tuple containing the actual action
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
""" """
raise NotImplementedError("") raise NotImplementedError("")

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): # for i in range(self.steps_limit.num_steps):
# let the agent observe the result and decide if it wants to terminate the episode # let the agent observe the result and decide if it wants to terminate the episode
done = acting_agent.emulate_observe_on_trainer(transition) 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: if done:
self.handle_episode_ended() self.handle_episode_ended()