mirror of
https://github.com/vale981/ray
synced 2025-03-07 02:51:39 -05:00
127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
import gym
|
|
from typing import Optional, Dict
|
|
|
|
import ray
|
|
from ray.rllib.agents.ppo.ppo_torch_policy import ValueNetworkMixin
|
|
from ray.rllib.evaluation.episode import MultiAgentEpisode
|
|
from ray.rllib.evaluation.postprocessing import compute_gae_for_sample_batch, \
|
|
Postprocessing
|
|
from ray.rllib.models.action_dist import ActionDistribution
|
|
from ray.rllib.models.modelv2 import ModelV2
|
|
from ray.rllib.policy.policy import Policy
|
|
from ray.rllib.policy.policy_template import build_policy_class
|
|
from ray.rllib.policy.sample_batch import SampleBatch
|
|
from ray.rllib.utils.annotations import Deprecated
|
|
from ray.rllib.utils.framework import try_import_torch
|
|
from ray.rllib.utils.torch_ops import apply_grad_clipping, sequence_mask
|
|
from ray.rllib.utils.typing import TrainerConfigDict, TensorType, \
|
|
PolicyID, LocalOptimizer
|
|
|
|
torch, nn = try_import_torch()
|
|
|
|
|
|
@Deprecated(
|
|
old="rllib.agents.a3c.a3c_torch_policy.add_advantages",
|
|
new="rllib.evaluation.postprocessing.compute_gae_for_sample_batch",
|
|
error=False)
|
|
def add_advantages(
|
|
policy: Policy,
|
|
sample_batch: SampleBatch,
|
|
other_agent_batches: Optional[Dict[PolicyID, SampleBatch]] = None,
|
|
episode: Optional[MultiAgentEpisode] = None) -> SampleBatch:
|
|
|
|
return compute_gae_for_sample_batch(policy, sample_batch,
|
|
other_agent_batches, episode)
|
|
|
|
|
|
def actor_critic_loss(policy: Policy, model: ModelV2,
|
|
dist_class: ActionDistribution,
|
|
train_batch: SampleBatch) -> TensorType:
|
|
logits, _ = model.from_batch(train_batch)
|
|
values = model.value_function()
|
|
|
|
if policy.is_recurrent():
|
|
max_seq_len = torch.max(train_batch["seq_lens"])
|
|
mask_orig = sequence_mask(train_batch["seq_lens"], max_seq_len)
|
|
valid_mask = torch.reshape(mask_orig, [-1])
|
|
else:
|
|
valid_mask = torch.ones_like(values, dtype=torch.bool)
|
|
|
|
dist = dist_class(logits, model)
|
|
log_probs = dist.logp(train_batch[SampleBatch.ACTIONS]).reshape(-1)
|
|
pi_err = -torch.sum(
|
|
torch.masked_select(log_probs * train_batch[Postprocessing.ADVANTAGES],
|
|
valid_mask))
|
|
|
|
# Compute a value function loss.
|
|
if policy.config["use_critic"]:
|
|
value_err = 0.5 * torch.sum(
|
|
torch.pow(
|
|
torch.masked_select(
|
|
values.reshape(-1) -
|
|
train_batch[Postprocessing.VALUE_TARGETS], valid_mask),
|
|
2.0))
|
|
# Ignore the value function.
|
|
else:
|
|
value_err = 0.0
|
|
|
|
entropy = torch.sum(torch.masked_select(dist.entropy(), valid_mask))
|
|
|
|
total_loss = (pi_err + value_err * policy.config["vf_loss_coeff"] -
|
|
entropy * policy.config["entropy_coeff"])
|
|
|
|
policy.entropy = entropy
|
|
policy.pi_err = pi_err
|
|
policy.value_err = value_err
|
|
|
|
return total_loss
|
|
|
|
|
|
def loss_and_entropy_stats(policy: Policy,
|
|
train_batch: SampleBatch) -> Dict[str, TensorType]:
|
|
return {
|
|
"policy_entropy": policy.entropy,
|
|
"policy_loss": policy.pi_err,
|
|
"vf_loss": policy.value_err,
|
|
}
|
|
|
|
|
|
def model_value_predictions(
|
|
policy: Policy, input_dict: Dict[str, TensorType], state_batches,
|
|
model: ModelV2,
|
|
action_dist: ActionDistribution) -> Dict[str, TensorType]:
|
|
return {SampleBatch.VF_PREDS: model.value_function()}
|
|
|
|
|
|
def torch_optimizer(policy: Policy,
|
|
config: TrainerConfigDict) -> LocalOptimizer:
|
|
return torch.optim.Adam(policy.model.parameters(), lr=config["lr"])
|
|
|
|
|
|
def setup_mixins(policy: Policy, obs_space: gym.spaces.Space,
|
|
action_space: gym.spaces.Space,
|
|
config: TrainerConfigDict) -> None:
|
|
"""Call all mixin classes' constructors before PPOPolicy initialization.
|
|
|
|
Args:
|
|
policy (Policy): The Policy object.
|
|
obs_space (gym.spaces.Space): The Policy's observation space.
|
|
action_space (gym.spaces.Space): The Policy's action space.
|
|
config (TrainerConfigDict): The Policy's config.
|
|
"""
|
|
ValueNetworkMixin.__init__(policy, obs_space, action_space, config)
|
|
|
|
|
|
A3CTorchPolicy = build_policy_class(
|
|
name="A3CTorchPolicy",
|
|
framework="torch",
|
|
get_default_config=lambda: ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG,
|
|
loss_fn=actor_critic_loss,
|
|
stats_fn=loss_and_entropy_stats,
|
|
postprocess_fn=compute_gae_for_sample_batch,
|
|
extra_action_out_fn=model_value_predictions,
|
|
extra_grad_process_fn=apply_grad_clipping,
|
|
optimizer_fn=torch_optimizer,
|
|
before_loss_init=setup_mixins,
|
|
mixins=[ValueNetworkMixin],
|
|
)
|