mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
e2edca45d4
* Take out stats to analyze memory leak in torch PPO. * WIP * WIP * WIP * WIP * WIP * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * WIP. * LINT. * Fix determine_tests_to_run.py. * minor change to re-test after determine_tests_to_run.py. * LINT. * update comments. * WIP * WIP * WIP * FIX. * Fix sequence_mask being dependent on torch being installed. * Fix strange ray-core tf-error in test_memory_scheduling test case. * Fix strange ray-core tf-error in test_memory_scheduling test case. * Fix strange ray-core tf-error in test_memory_scheduling test case. * Fix strange ray-core tf-error in test_memory_scheduling test case.
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
import ray
|
|
from ray.rllib.evaluation.postprocessing import compute_advantages, \
|
|
Postprocessing
|
|
from ray.rllib.policy.sample_batch import SampleBatch
|
|
from ray.rllib.policy.torch_policy_template import build_torch_policy
|
|
from ray.rllib.utils.framework import try_import_torch
|
|
|
|
torch, nn = try_import_torch()
|
|
|
|
|
|
def actor_critic_loss(policy, model, dist_class, train_batch):
|
|
logits, _ = model.from_batch(train_batch)
|
|
values = model.value_function()
|
|
dist = dist_class(logits, model)
|
|
log_probs = dist.logp(train_batch[SampleBatch.ACTIONS])
|
|
policy.entropy = dist.entropy().mean()
|
|
policy.pi_err = -train_batch[Postprocessing.ADVANTAGES].dot(
|
|
log_probs.reshape(-1))
|
|
policy.value_err = nn.functional.mse_loss(
|
|
values.reshape(-1), train_batch[Postprocessing.VALUE_TARGETS])
|
|
overall_err = sum([
|
|
policy.pi_err,
|
|
policy.config["vf_loss_coeff"] * policy.value_err,
|
|
-policy.config["entropy_coeff"] * policy.entropy,
|
|
])
|
|
return overall_err
|
|
|
|
|
|
def loss_and_entropy_stats(policy, train_batch):
|
|
return {
|
|
"policy_entropy": policy.entropy.item(),
|
|
"policy_loss": policy.pi_err.item(),
|
|
"vf_loss": policy.value_err.item(),
|
|
}
|
|
|
|
|
|
def add_advantages(policy,
|
|
sample_batch,
|
|
other_agent_batches=None,
|
|
episode=None):
|
|
|
|
completed = sample_batch[SampleBatch.DONES][-1]
|
|
if completed:
|
|
last_r = 0.0
|
|
else:
|
|
last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1])
|
|
|
|
return compute_advantages(sample_batch, last_r, policy.config["gamma"],
|
|
policy.config["lambda"],
|
|
policy.config["use_gae"],
|
|
policy.config["use_critic"])
|
|
|
|
|
|
def model_value_predictions(policy, input_dict, state_batches, model,
|
|
action_dist):
|
|
return {SampleBatch.VF_PREDS: model.value_function()}
|
|
|
|
|
|
def apply_grad_clipping(policy):
|
|
info = {}
|
|
if policy.config["grad_clip"]:
|
|
total_norm = nn.utils.clip_grad_norm_(policy.model.parameters(),
|
|
policy.config["grad_clip"])
|
|
info["grad_gnorm"] = total_norm
|
|
return info
|
|
|
|
|
|
def torch_optimizer(policy, config):
|
|
return torch.optim.Adam(policy.model.parameters(), lr=config["lr"])
|
|
|
|
|
|
class ValueNetworkMixin:
|
|
def _value(self, obs):
|
|
_ = self.model({"obs": torch.Tensor([obs]).to(self.device)}, [], [1])
|
|
return self.model.value_function()[0]
|
|
|
|
|
|
A3CTorchPolicy = build_torch_policy(
|
|
name="A3CTorchPolicy",
|
|
get_default_config=lambda: ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG,
|
|
loss_fn=actor_critic_loss,
|
|
stats_fn=loss_and_entropy_stats,
|
|
postprocess_fn=add_advantages,
|
|
extra_action_out_fn=model_value_predictions,
|
|
extra_grad_process_fn=apply_grad_clipping,
|
|
optimizer_fn=torch_optimizer,
|
|
mixins=[ValueNetworkMixin])
|