def add_policy(self, parsed_behavior_id: BehaviorIdentifiers,
policy: TFPolicy) -> None:
"""
Adds policy to trainer. The first policy encountered sets the wrapped
trainer team. This is to ensure that all agents from the same multi-agent
team are grouped. All policies associated with this team are added to the
wrapped trainer to be trained.
:param name_behavior_id: Behavior ID that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
name_behavior_id = parsed_behavior_id.behavior_id
team_id = parsed_behavior_id.team_id
self.controller.subscribe_team_id(team_id, self)
self.policies[name_behavior_id] = policy
policy.create_tf_graph()
self._name_to_parsed_behavior_id[name_behavior_id] = parsed_behavior_id
# for saving/swapping snapshots
policy.init_load_weights()
# First policy or a new agent on the same team encountered
if self.wrapped_trainer_team is None or team_id == self.wrapped_trainer_team:
self.current_policy_snapshot[
parsed_behavior_id.brain_name] = policy.get_weights()
self._save_snapshot(
) # Need to save after trainer initializes policy
self.trainer.add_policy(parsed_behavior_id, policy)
self._learning_team = self.controller.get_learning_team
self.wrapped_trainer_team = team_id
def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
"""
Adds policy to trainer. For the first policy added, add a trainer
to the policy and set the learning behavior name to name_behavior_id.
:param name_behavior_id: Behavior ID that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
self.policies[name_behavior_id] = policy
policy.create_tf_graph()
# First policy encountered
if not self.learning_behavior_name:
weights = policy.get_weights()
self.current_policy_snapshot = weights
self.trainer.add_policy(name_behavior_id, policy)
self._save_snapshot(
policy) # Need to save after trainer initializes policy
self.learning_behavior_name = name_behavior_id
behavior_id_parsed = BehaviorIdentifiers.from_name_behavior_id(
self.learning_behavior_name)
team_id = behavior_id_parsed.behavior_ids["team"]
self._stats_reporter.add_property(StatsPropertyType.SELF_PLAY_TEAM,
team_id)
else:
# for saving/swapping snapshots
policy.init_load_weights()
def _save_snapshot(self, policy: TFPolicy) -> None:
weights = policy.get_weights()
try:
self.policy_snapshots[self.snapshot_counter] = weights
except IndexError:
self.policy_snapshots.append(weights)
self.policy_elos[self.snapshot_counter] = self.current_elo
self.snapshot_counter = (self.snapshot_counter + 1) % self.window
def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
"""
Adds policy to trainer. For the first policy added, add a trainer
to the policy and set the learning behavior name to name_behavior_id.
:param name_behavior_id: Behavior ID that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
self.policies[name_behavior_id] = policy
policy.create_tf_graph()
# First policy encountered
if not self.learning_behavior_name:
weights = policy.get_weights()
self.current_policy_snapshot = weights
self.trainer.add_policy(name_behavior_id, policy)
self._save_snapshot(policy) # Need to save after trainer initializes policy
self.learning_behavior_name = name_behavior_id
else:
# for saving/swapping snapshots
policy.init_load_weights()
