def setUp(self): super(PpoTest, self).setUp() self.rng_key = trainer_lib.init_random_number_generators(0)
def setUp(self):
super(PolicyBasedUtilsTest, self).setUp()
self.rng_key = trainer_lib.init_random_number_generators(0)
def __init__(
self,
train_env,
eval_env,
output_dir=None,
random_seed=None,
controller=None,
# Policy and Value model arguments.
policy_and_value_model=None,
policy_and_value_optimizer=None,
policy_and_value_two_towers=False,
policy_and_value_vocab_size=None,
init_policy_from_world_model_output_dir=None,
# Trajectory collection arguments.
boundary=20,
max_timestep=100,
max_timestep_eval=20000,
len_history_for_policy=4,
# Save / Restore arguments.
should_save_checkpoints=True,
should_write_summaries=True,
eval_every_n=1000,
save_every_n=1000,
done_frac_for_policy_save=0.5,
# Eval arguments.
n_evals=1,
eval_temperatures=(1.0, 0.5),
separate_eval=True,
# Optimization arguments.
n_optimizer_steps=N_OPTIMIZER_STEPS,
optimizer_batch_size=64,
**kwargs):
"""Creates the PolicyBasedTrainer.
Args:
train_env: gym.Env to use for training.
eval_env: gym.Env to use for evaluation.
output_dir: Output dir.
random_seed: Random seed.
controller: Function history -> (step -> {'name': value}) controlling
nontrainable parameters.
policy_and_value_model: Function defining the policy and value network,
without the policy and value heads.
policy_and_value_optimizer: Function defining the optimizer.
policy_and_value_two_towers: Whether to use two separate models as the
policy and value networks. If False, share their parameters.
policy_and_value_vocab_size: Vocabulary size of a policy and value network
operating on serialized representation. If None, use raw continuous
representation.
init_policy_from_world_model_output_dir: Model output dir for initializing
the policy. If None, initialize randomly.
boundary: We pad trajectories at integer multiples of this number.
max_timestep: If set to an integer, maximum number of time-steps in a
trajectory. Used in the collect procedure.
max_timestep_eval: If set to an integer, maximum number of time-steps in
an evaluation trajectory. Used in the collect procedure.
len_history_for_policy: How much of history to give to the policy.
should_save_checkpoints: Whether to save policy checkpoints.
should_write_summaries: Whether to save summaries.
eval_every_n: How frequently to eval the policy.
save_every_n: How frequently to save the policy.
done_frac_for_policy_save: Fraction of the trajectories that should be
done to checkpoint the policy.
n_evals: Number of times to evaluate.
eval_temperatures: Sequence of temperatures to try for categorical
sampling during evaluation.
separate_eval: Whether to run separate evaluation using a set of
temperatures. If False, the training reward is reported as evaluation
reward with temperature 1.0.
n_optimizer_steps: Number of optimizer steps.
optimizer_batch_size: Batch size of an optimizer step.
**kwargs: Additional keyword arguments passed to the base class.
"""
super(PolicyBasedTrainer, self).__init__(train_env, eval_env,
output_dir, **kwargs)
self._rng = trainer_lib.init_random_number_generators(random_seed)
self._controller = controller
self._history = None
self._epoch = 0
# Trajectory collection arguments.
self._boundary = boundary
self._max_timestep = max_timestep
self._max_timestep_eval = max_timestep_eval
self._len_history_for_policy = len_history_for_policy
# Save / Restore arguments.
self._should_save_checkpoints = should_save_checkpoints
self._should_write_summaries = should_write_summaries
self._train_sw, self._eval_sw, self._timing_sw = None, None, None
self._eval_every_n = eval_every_n
self._save_every_n = save_every_n
self._done_frac_for_policy_save = done_frac_for_policy_save
self._n_trajectories_done_since_last_save = 0
self._last_saved_at_epoch = self._epoch
# Eval arguments.
self._n_evals = n_evals
self._eval_temperatures = eval_temperatures
self._separate_eval = separate_eval
# Optimization arguments.
self._n_optimizer_steps = n_optimizer_steps
self._optimizer_batch_size = optimizer_batch_size
self._total_opt_step = 0
# Policy and Value model arguments.
self._policy_and_value_vocab_size = policy_and_value_vocab_size
self._serialization_kwargs = {}
if self._policy_and_value_vocab_size is not None:
self._serialization_kwargs = ppo.init_serialization(
vocab_size=self._policy_and_value_vocab_size,
observation_space=train_env.observation_space,
action_space=train_env.action_space,
n_timesteps=(self._max_timestep + 1),
)
self._init_policy_from_world_model_output_dir = (
init_policy_from_world_model_output_dir)
self._rewards_to_actions = ppo.init_rewards_to_actions(
self._policy_and_value_vocab_size,
train_env.observation_space,
train_env.action_space,
n_timesteps=(self._max_timestep + 1),
)
(n_controls, n_actions) = serialization_utils.analyze_action_space(
train_env.action_space)
self._policy_and_value_net_fn = functools.partial(
ppo.policy_and_value_net,
n_actions=n_actions,
n_controls=n_controls,
vocab_size=self._policy_and_value_vocab_size,
bottom_layers_fn=policy_and_value_model,
two_towers=policy_and_value_two_towers,
)
self._policy_and_value_net_apply = jit(self._policy_and_value_net_fn())
self._policy_and_value_optimizer = policy_and_value_optimizer()
self._model_state = None
self._policy_and_value_opt_state = None
def __init__(self,
train_env,
eval_env,
output_dir=None,
policy_and_value_model=trax_models.FrameStackMLP,
policy_and_value_optimizer=functools.partial(
trax_opt.Adam, learning_rate=1e-3),
policy_and_value_two_towers=False,
policy_and_value_vocab_size=None,
n_optimizer_steps=N_OPTIMIZER_STEPS,
optimizer_batch_size=64,
print_every_optimizer_steps=PRINT_EVERY_OPTIMIZER_STEP,
target_kl=0.01,
boundary=20,
max_timestep=100,
max_timestep_eval=20000,
random_seed=None,
gamma=GAMMA,
lambda_=LAMBDA,
value_weight=1.0,
entropy_weight=0.01,
epsilon=0.1,
eval_every_n=1000,
save_every_n=1000,
done_frac_for_policy_save=0.5,
n_evals=1,
len_history_for_policy=4,
eval_temperatures=(1.0, 0.5),
separate_eval=True,
init_policy_from_world_model_output_dir=None,
controller=None,
should_save_checkpoints=True,
should_write_summaries=True,
**kwargs):
"""Creates the PPO trainer.
Args:
train_env: gym.Env to use for training.
eval_env: gym.Env to use for evaluation.
output_dir: Output dir.
policy_and_value_model: Function defining the policy and value network,
without the policy and value heads.
policy_and_value_optimizer: Function defining the optimizer.
policy_and_value_two_towers: Whether to use two separate models as the
policy and value networks. If False, share their parameters.
policy_and_value_vocab_size: Vocabulary size of a policy and value network
operating on serialized representation. If None, use raw continuous
representation.
n_optimizer_steps: Number of optimizer steps.
optimizer_batch_size: Batch size of an optimizer step.
print_every_optimizer_steps: How often to log during the policy
optimization process.
target_kl: Policy iteration early stopping. Set to infinity to disable
early stopping.
boundary: We pad trajectories at integer multiples of this number.
max_timestep: If set to an integer, maximum number of time-steps in a
trajectory. Used in the collect procedure.
max_timestep_eval: If set to an integer, maximum number of time-steps in
an evaluation trajectory. Used in the collect procedure.
random_seed: Random seed.
gamma: Reward discount factor.
lambda_: N-step TD-error discount factor in GAE.
value_weight: Value loss coefficient.
entropy_weight: Entropy loss coefficient.
epsilon: Clipping coefficient.
eval_every_n: How frequently to eval the policy.
save_every_n: How frequently to save the policy.
done_frac_for_policy_save: Fraction of the trajectories that should be
done to checkpoint the policy.
n_evals: Number of times to evaluate.
len_history_for_policy: How much of history to give to the policy.
eval_temperatures: Sequence of temperatures to try for categorical
sampling during evaluation.
separate_eval: Whether to run separate evaluation using a set of
temperatures. If False, the training reward is reported as evaluation
reward with temperature 1.0.
init_policy_from_world_model_output_dir: Model output dir for initializing
the policy. If None, initialize randomly.
controller: Function history -> (step -> {'name': value}) controlling
nontrainable parameters.
should_save_checkpoints: Whether to save policy checkpoints.
should_write_summaries: Whether to save summaries.
**kwargs: Additional keyword arguments passed to the base class.
"""
# Set in base class constructor.
self._train_env = None
self._should_reset = None
self._n_optimizer_steps = n_optimizer_steps
self._optimizer_batch_size = optimizer_batch_size
self._print_every_optimizer_steps = print_every_optimizer_steps
self._target_kl = target_kl
self._boundary = boundary
self._max_timestep = max_timestep
self._max_timestep_eval = max_timestep_eval
self._nontrainable_params = {
'gamma': np.array(gamma),
'lambda': np.array(lambda_),
'value_weight': np.array(value_weight),
'entropy_weight': np.array(entropy_weight),
'epsilon': np.array(epsilon),
}
self._eval_every_n = eval_every_n
self._save_every_n = save_every_n
self._done_frac_for_policy_save = done_frac_for_policy_save
self._n_evals = n_evals
self._len_history_for_policy = len_history_for_policy
self._eval_temperatures = eval_temperatures
self._separate_eval = separate_eval
self._controller = controller
self._should_save_checkpoints = should_save_checkpoints
self._should_write_summaries = should_write_summaries
self._history = None
(n_controls, n_actions) = ppo.analyze_action_space(train_env.action_space)
self._rng = trainer_lib.init_random_number_generators(random_seed)
self._policy_and_value_vocab_size = policy_and_value_vocab_size
if self._policy_and_value_vocab_size is not None:
self._serialization_kwargs = ppo.init_serialization(
vocab_size=self._policy_and_value_vocab_size,
observation_space=train_env.observation_space,
action_space=train_env.action_space,
n_timesteps=(self._max_timestep + 1),
)
else:
self._serialization_kwargs = {}
self.init_policy_from_world_model_output_dir = (
init_policy_from_world_model_output_dir
)
self._rewards_to_actions = ppo.init_rewards_to_actions(
self._policy_and_value_vocab_size,
train_env.observation_space,
train_env.action_space,
n_timesteps=(self._max_timestep + 1),
)
self._policy_and_value_net_fn = functools.partial(
ppo.policy_and_value_net,
n_actions=n_actions,
n_controls=n_controls,
vocab_size=self._policy_and_value_vocab_size,
bottom_layers_fn=policy_and_value_model,
two_towers=policy_and_value_two_towers,
)
self._policy_and_value_net_apply = jit(self._policy_and_value_net_fn())
self._policy_and_value_optimizer = policy_and_value_optimizer()
# Super ctor calls reset(), which uses fields initialized above.
super(PPO, self).__init__(train_env, eval_env, output_dir, **kwargs)
