def add_policy(
self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy
) -> None:
"""
Adds policy to trainer.
"""
if self.policy:
logger.warning(
"Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
train adversarial games.".format(
self.__class__.__name__
)
)
self.policy = policy
self.policies[parsed_behavior_id.behavior_id] = policy
self.optimizer = SACOptimizer(self.policy, self.trainer_settings)
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
# Needed to resume loads properly
self.step = policy.get_current_step()
# Assume steps were updated at the correct ratio before
self.update_steps = int(max(1, self.step / self.steps_per_update))
self.reward_signal_update_steps = int(
max(1, self.step / self.reward_signal_steps_per_update)
)
def test_step_overflow():
behavior_spec = mb.setup_test_behavior_specs(use_discrete=True,
use_visual=False,
vector_action_space=[2],
vector_obs_space=1)
policy = TFPolicy(
0,
behavior_spec,
TrainerSettings(network_settings=NetworkSettings(normalize=True)),
create_tf_graph=False,
)
policy.create_input_placeholders()
policy.initialize()
policy.set_step(2**31 - 1)
assert policy.get_current_step() == 2**31 - 1
policy.increment_step(3)
assert policy.get_current_step() == 2**31 + 2
def add_policy(self, parsed_behavior_id: BehaviorIdentifiers,
policy: TFPolicy) -> None:
"""
Adds policy to trainer.
:param parsed_behavior_id: Behavior identifiers that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
if self.policy:
logger.warning(
"Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
train adversarial games.".format(self.__class__.__name__))
self.policy = policy
self.policies[parsed_behavior_id.behavior_id] = policy
self.optimizer = PPOOptimizer(self.policy, self.trainer_settings)
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
# Needed to resume loads properly
self.step = policy.get_current_step()
def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
"""
Adds policy to trainer.
:param brain_parameters: specifications for policy construction
"""
if self.policy:
logger.warning(
"add_policy has been called twice. {} is not a multi-agent trainer"
.format(self.__class__.__name__))
if not isinstance(policy, NNPolicy):
raise RuntimeError(
"Non-SACPolicy passed to SACTrainer.add_policy()")
self.policy = policy
self.optimizer = SACOptimizer(self.policy, self.trainer_parameters)
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
# Needed to resume loads properly
self.step = policy.get_current_step()
self.next_summary_step = self._get_next_summary_step()
def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
"""
Adds policy to trainer.
:param brain_parameters: specifications for policy construction
"""
if self.policy:
logger.warning(
"Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
train adversarial games.".format(self.__class__.__name__))
if not isinstance(policy, NNPolicy):
raise RuntimeError(
"Non-SACPolicy passed to SACTrainer.add_policy()")
self.policy = policy
self.optimizer = SACOptimizer(self.policy, self.trainer_parameters)
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
# Needed to resume loads properly
self.step = policy.get_current_step()
self.next_summary_step = self._get_next_summary_step()
def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
"""
Adds policy to trainer.
:param name_behavior_id: Behavior ID that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
if self.policy:
logger.warning(
"add_policy has been called twice. {} is not a multi-agent trainer"
.format(self.__class__.__name__))
if not isinstance(policy, NNPolicy):
raise RuntimeError(
"Non-NNPolicy passed to PPOTrainer.add_policy()")
self.policy = policy
self.optimizer = PPOOptimizer(self.policy, self.trainer_parameters)
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
# Needed to resume loads properly
self.step = policy.get_current_step()
self.next_summary_step = self._get_next_summary_step()
def add_policy(self, parsed_behavior_id: BehaviorIdentifiers,
policy: TFPolicy) -> None:
"""
Adds policy to trainer.
:param brain_parameters: specifications for policy construction
"""
if self.policy:
logger.warning(
"Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \
train adversarial games.".format(self.__class__.__name__))
if not isinstance(policy, NNPolicy):
raise RuntimeError(
"Non-SACPolicy passed to SACTrainer.add_policy()")
self.policy = policy
self.optimizer = SACOptimizer(self.policy, self.trainer_settings)
for _reward_signal in self.optimizer.reward_signals.keys():
self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
# Needed to resume loads properly
self.step = policy.get_current_step()
# Assume steps were updated at the correct ratio before
self.update_steps = int(max(1, self.step / self.steps_per_update))
self.reward_signal_update_steps = int(
max(1, self.step / self.reward_signal_steps_per_update))
