def __init__(self, obs_space, action_space, config):
if get_default_config:
config = dict(get_default_config(), **config)
self.config = config
if before_init:
before_init(self, obs_space, action_space, config)
if make_model_and_action_dist:
self.model, self.dist_class = make_model_and_action_dist(
self, obs_space, action_space, config)
else:
self.dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"], torch=True)
self.model = ModelCatalog.get_model_v2(obs_space,
action_space,
logit_dim,
self.config["model"],
framework="torch")
TorchPolicy.__init__(self, obs_space, action_space, config,
self.model, loss_fn, self.dist_class)
if after_init:
after_init(self, obs_space, action_space, config)
def __init__(self, obs_space, action_space, config):
if get_default_config:
config = dict(get_default_config(), **config)
self.config = config
if before_init:
before_init(self, obs_space, action_space, config)
if make_model_and_action_dist:
self.model, self.dist_class = make_model_and_action_dist(
self, obs_space, action_space, config)
# Make sure, we passed in a correct Model factory.
assert isinstance(self.model, TorchModelV2), \
"ERROR: TorchPolicy::make_model_and_action_dist must " \
"return a TorchModelV2 object!"
else:
self.dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"], framework="torch")
self.model = ModelCatalog.get_model_v2(obs_space,
action_space,
logit_dim,
self.config["model"],
framework="torch")
TorchPolicy.__init__(self, obs_space, action_space, config,
self.model, loss_fn, self.dist_class)
if after_init:
after_init(self, obs_space, action_space, config)
def __init__(self, observation_space, action_space, config):
config = dict(ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG, **config)
setup_config(self, observation_space, action_space, config)
TorchPolicy.__init__(
self,
observation_space,
action_space,
config,
max_seq_len=config["model"]["max_seq_len"],
)
ValueNetworkMixin.__init__(self, config)
EntropyCoeffSchedule.__init__(self, config["entropy_coeff"],
config["entropy_coeff_schedule"])
LearningRateSchedule.__init__(self, config["lr"],
config["lr_schedule"])
# The current KL value (as python float).
self.kl_coeff = self.config["kl_coeff"]
# Constant target value.
self.kl_target = self.config["kl_target"]
# TODO: Don't require users to call this manually.
self._initialize_loss_from_dummy_batch()
def __init__(self, obs_space, action_space, config):
if get_default_config:
config = dict(get_default_config(), **config)
self.config = config
if validate_spaces:
validate_spaces(self, obs_space, action_space, self.config)
if before_init:
before_init(self, obs_space, action_space, self.config)
# Model is customized (use default action dist class).
if make_model:
assert make_model_and_action_dist is None, \
"Either `make_model` or `make_model_and_action_dist`" \
" must be None!"
self.model = make_model(self, obs_space, action_space, config)
dist_class, _ = ModelCatalog.get_action_dist(
action_space, self.config["model"], framework="torch")
# Model and action dist class are customized.
elif make_model_and_action_dist:
self.model, dist_class = make_model_and_action_dist(
self, obs_space, action_space, config)
# Use default model and default action dist.
else:
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"], framework="torch")
self.model = ModelCatalog.get_model_v2(
obs_space=obs_space,
action_space=action_space,
num_outputs=logit_dim,
model_config=self.config["model"],
framework="torch")
# Make sure, we passed in a correct Model factory.
assert isinstance(self.model, TorchModelV2), \
"ERROR: Generated Model must be a TorchModelV2 object!"
TorchPolicy.__init__(
self,
observation_space=obs_space,
action_space=action_space,
config=config,
model=self.model,
loss=loss_fn,
action_distribution_class=dist_class,
action_sampler_fn=action_sampler_fn,
action_distribution_fn=action_distribution_fn,
max_seq_len=config["model"]["max_seq_len"],
get_batch_divisibility_req=get_batch_divisibility_req,
)
if callable(training_view_requirements_fn):
self.training_view_requirements.update(
training_view_requirements_fn(self))
if after_init:
after_init(self, obs_space, action_space, config)
def extra_action_out(self, input_dict, state_batches, model_out):
if extra_action_out_fn:
return extra_action_out_fn(self, input_dict, state_batches,
model_out)
else:
return TorchPolicy.extra_action_out(self, input_dict,
state_batches, model_out)
def extra_grad_info(self, train_batch):
with torch.no_grad():
if stats_fn:
stats_dict = stats_fn(self, train_batch)
else:
stats_dict = TorchPolicy.extra_grad_info(self, train_batch)
return convert_to_non_torch_type(stats_dict)
def extra_compute_grad_fetches(self):
if extra_learn_fetches_fn:
fetches = convert_to_non_torch_type(
extra_learn_fetches_fn(self))
# Auto-add empty learner stats dict if needed.
return dict({LEARNER_STATS_KEY: {}}, **fetches)
else:
return TorchPolicy.extra_compute_grad_fetches(self)
def optimizer(self):
if optimizer_fn:
optimizers = optimizer_fn(self, self.config)
else:
optimizers = TorchPolicy.optimizer(self)
optimizers = force_list(optimizers)
if getattr(self, "exploration", None):
optimizers = self.exploration.get_exploration_optimizer(
optimizers)
return optimizers
def extra_action_out(self, input_dict, state_batches, model,
action_dist):
with torch.no_grad():
if extra_action_out_fn:
stats_dict = extra_action_out_fn(
self, input_dict, state_batches, model, action_dist)
else:
stats_dict = TorchPolicy.extra_action_out(
self, input_dict, state_batches, model, action_dist)
return convert_to_non_torch_type(stats_dict)
def extra_grad_process(self, optimizer, loss):
"""Called after optimizer.zero_grad() and loss.backward() calls.
Allows for gradient processing before optimizer.step() is called.
E.g. for gradient clipping.
"""
if extra_grad_process_fn:
return extra_grad_process_fn(self, optimizer, loss)
else:
return TorchPolicy.extra_grad_process(self, optimizer, loss)
def __init__(self, obs_space, action_space, config):
if get_default_config:
config = dict(get_default_config(), **config)
self.config = config
if before_init:
before_init(self, obs_space, action_space, config)
if make_model_and_action_dist:
self.model, dist_class = make_model_and_action_dist(
self, obs_space, action_space, config)
# Make sure, we passed in a correct Model factory.
assert isinstance(self.model, TorchModelV2), \
"ERROR: TorchPolicy::make_model_and_action_dist must " \
"return a TorchModelV2 object!"
else:
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"], framework="torch")
self.model = ModelCatalog.get_model_v2(
obs_space=obs_space,
action_space=action_space,
num_outputs=logit_dim,
model_config=self.config["model"],
framework="torch",
**self.config["model"].get("custom_options", {}))
TorchPolicy.__init__(
self,
obs_space,
action_space,
config,
model=self.model,
loss=loss_fn,
action_distribution_class=dist_class,
action_sampler_fn=action_sampler_fn,
action_distribution_fn=action_distribution_fn,
max_seq_len=config["model"]["max_seq_len"],
get_batch_divisibility_req=get_batch_divisibility_req,
)
if after_init:
after_init(self, obs_space, action_space, config)
def optimizer(self):
if optimizer_fn:
optimizers = optimizer_fn(self, self.config)
else:
optimizers = TorchPolicy.optimizer(self)
optimizers = force_list(optimizers)
if hasattr(self, "exploration"):
exploration_optimizers = force_list(
self.exploration.get_exploration_optimizer(self.config))
optimizers.extend(exploration_optimizers)
return optimizers
def set_weights(self, weights):
# Makes sure that whenever we restore weights for this policy's
# model, we sync the target network (from the main model)
# at the same time.
TorchPolicy.set_weights(self, weights)
self.update_target()
def extra_grad_info(self, batch_tensors):
if stats_fn:
return stats_fn(self, batch_tensors)
else:
return TorchPolicy.extra_grad_info(self, batch_tensors)
def optimizer(self):
if optimizer_fn:
return optimizer_fn(self, self.config)
else:
return TorchPolicy.optimizer(self)
def extra_grad_process(self):
if extra_grad_process_fn:
return extra_grad_process_fn(self)
else:
return TorchPolicy.extra_grad_process(self)
def extra_grad_info(self, train_batch):
if stats_fn:
return stats_fn(self, train_batch)
else:
return TorchPolicy.extra_grad_info(self, train_batch)
def apply_gradients(self, gradients):
if apply_gradients_fn:
apply_gradients_fn(self, gradients)
else:
TorchPolicy.apply_gradients(self, gradients)
def extra_action_out(self, model_out):
if extra_action_out_fn:
return extra_action_out_fn(self, model_out)
else:
return TorchPolicy.extra_action_out(self, model_out)
def __init__(self, obs_space, action_space, config):
if get_default_config:
config = dict(get_default_config(), **config)
self.config = config
if validate_spaces:
validate_spaces(self, obs_space, action_space, self.config)
if before_init:
before_init(self, obs_space, action_space, self.config)
# Model is customized (use default action dist class).
if make_model:
assert make_model_and_action_dist is None, \
"Either `make_model` or `make_model_and_action_dist`" \
" must be None!"
self.model = make_model(self, obs_space, action_space, config)
dist_class, _ = ModelCatalog.get_action_dist(
action_space, self.config["model"], framework="torch")
# Model and action dist class are customized.
elif make_model_and_action_dist:
self.model, dist_class = make_model_and_action_dist(
self, obs_space, action_space, config)
# Use default model and default action dist.
else:
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"], framework="torch")
self.model = ModelCatalog.get_model_v2(
obs_space=obs_space,
action_space=action_space,
num_outputs=logit_dim,
model_config=self.config["model"],
framework="torch")
# Make sure, we passed in a correct Model factory.
assert isinstance(self.model, TorchModelV2), \
"ERROR: Generated Model must be a TorchModelV2 object!"
TorchPolicy.__init__(
self,
observation_space=obs_space,
action_space=action_space,
config=config,
model=self.model,
loss=loss_fn,
action_distribution_class=dist_class,
action_sampler_fn=action_sampler_fn,
action_distribution_fn=action_distribution_fn,
max_seq_len=config["model"]["max_seq_len"],
get_batch_divisibility_req=get_batch_divisibility_req,
)
# Update this Policy's ViewRequirements (if function given).
if callable(view_requirements_fn):
self.view_requirements.update(view_requirements_fn(self))
# Merge Model's view requirements into Policy's.
self.view_requirements.update(
self.model.inference_view_requirements)
_before_loss_init = before_loss_init or after_init
if _before_loss_init:
_before_loss_init(self, self.observation_space,
self.action_space, config)
# Perform test runs through postprocessing- and loss functions.
self._initialize_loss_from_dummy_batch(
auto_remove_unneeded_view_reqs=True,
stats_fn=stats_fn,
)
if _after_loss_init:
_after_loss_init(self, obs_space, action_space, config)
# Got to reset global_timestep again after this fake run-through.
self.global_timestep = 0
