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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from ray.rllib.agents.a3c.a3c_tf_policy_graph import A3CPolicyGraph
from ray.rllib.agents.agent import Agent, with_common_config
from ray.rllib.optimizers import AsyncGradientsOptimizer
from ray.rllib.utils.annotations import override
# yapf: disable
# __sphinx_doc_begin__
DEFAULT_CONFIG = with_common_config({
# Size of rollout batch
"sample_batch_size": 10,
# Use PyTorch as backend - no LSTM support
"use_pytorch": False,
# GAE(gamma) parameter
"lambda": 1.0,
# Max global norm for each gradient calculated by worker
"grad_clip": 40.0,
# Learning rate
"lr": 0.0001,
# Learning rate schedule
"lr_schedule": None,
# Value Function Loss coefficient
"vf_loss_coeff": 0.5,
# Entropy coefficient
"entropy_coeff": -0.01,
# Min time per iteration
"min_iter_time_s": 5,
# Workers sample async. Note that this increases the effective
# sample_batch_size by up to 5x due to async buffering of batches.
"sample_async": True,
# __sphinx_doc_end__
# yapf: enable
class A3CAgent(Agent):
"""A3C implementations in TensorFlow and PyTorch."""
_agent_name = "A3C"
_default_config = DEFAULT_CONFIG
_policy_graph = A3CPolicyGraph
def _init(self):
if self.config["use_pytorch"]:
from ray.rllib.agents.a3c.a3c_torch_policy_graph import \
policy_cls = A3CTorchPolicyGraph
policy_cls = self._policy_graph
self.local_evaluator = self.make_local_evaluator(
self.env_creator, policy_cls)
self.remote_evaluators = self.make_remote_evaluators(
self.env_creator, policy_cls, self.config["num_workers"])
self.optimizer = self._make_optimizer()
def _train(self):
prev_steps = self.optimizer.num_steps_sampled
start = time.time()
while time.time() - start < self.config["min_iter_time_s"]:
result = self.optimizer.collect_metrics(
result.update(timesteps_this_iter=self.optimizer.num_steps_sampled -
return result
def _make_optimizer(self):
return AsyncGradientsOptimizer(self.local_evaluator,