def __init__(self, obs_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
self.config = config
_, self.logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_torch_model(obs_space, self.logit_dim,
self.config["model"])
loss = PGLoss(self.model)
TorchPolicyGraph.__init__(self,
obs_space,
action_space,
self.model,
loss,
loss_inputs=["obs", "actions", "advantages"])
def __init__(self, obs_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
self.config = config
_, self.logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_torch_model(
obs_space.shape, self.logit_dim, self.config["model"])
loss = A3CLoss(self.model, self.config["vf_loss_coeff"],
self.config["entropy_coeff"])
TorchPolicyGraph.__init__(
self,
obs_space,
action_space,
self.model,
loss,
loss_inputs=["obs", "actions", "advantages", "value_targets"])
def __init__(self, obs_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
self.config = config
_, self.logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_torch_model(obs_space, self.logit_dim,
self.config["model"])
loss = A3CLoss(self.model, self.config["vf_loss_coeff"],
self.config["entropy_coeff"])
TorchPolicyGraph.__init__(
self,
obs_space,
action_space,
self.model,
loss,
loss_inputs=["obs", "actions", "advantages", "value_targets"])
def __init__(self, obs_space, action_space, config):
config = dict(ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG, **config)
self.config = config
_, self.logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
self.model = ModelCatalog.get_torch_model(obs_space, self.logit_dim,
self.config["model"])
loss = PGLoss(self.model)
TorchPolicyGraph.__init__(
self,
obs_space,
action_space,
self.model,
loss,
loss_inputs=[
SampleBatch.CUR_OBS, SampleBatch.ACTIONS,
Postprocessing.ADVANTAGES
])
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_torch_model(
obs_space, logit_dim, self.config["model"])
TorchPolicyGraph.__init__(self, obs_space, action_space,
self.model, loss_fn, self.dist_class)
if after_init:
after_init(self, obs_space, action_space, config)
def extra_grad_info(self, batch_tensors):
if stats_fn:
return stats_fn(self, batch_tensors)
else:
return TorchPolicyGraph.extra_grad_info(self, batch_tensors)
def optimizer(self):
if optimizer_fn:
return optimizer_fn(self, self.config)
else:
return TorchPolicyGraph.optimizer(self)
def extra_action_out(self, model_out):
if extra_action_out_fn:
return extra_action_out_fn(self, model_out)
else:
return TorchPolicyGraph.extra_action_out(self, model_out)
def extra_grad_process(self):
if extra_grad_process_fn:
return extra_grad_process_fn(self)
else:
return TorchPolicyGraph.extra_grad_process(self)