mirror of
https://github.com/wassname/ray.git
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Robert Nishihara
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from ray.rllib.agents.agent import Agent, with_common_config
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from ray.rllib.agents.pg.pg_policy_graph import PGPolicyGraph
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from ray.rllib.optimizers import SyncSamplesOptimizer
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from ray.rllib.utils import merge_dicts
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from ray.tune.trial import Resources
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# yapf: disable
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# __sphinx_doc_begin__
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DEFAULT_CONFIG = with_common_config({
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# No remote workers by default
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"num_workers": 0,
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# Learning rate
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"lr": 0.0004,
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})
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# __sphinx_doc_end__
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# yapf: enable
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class PGAgent(Agent):
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"""Simple policy gradient agent.
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This is an example agent to show how to implement algorithms in RLlib.
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In most cases, you will probably want to use the PPO agent instead.
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"""
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_agent_name = "PG"
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_default_config = DEFAULT_CONFIG
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_policy_graph = PGPolicyGraph
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@classmethod
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def default_resource_request(cls, config):
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cf = merge_dicts(cls._default_config, config)
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return Resources(cpu=1, gpu=0, extra_cpu=cf["num_workers"])
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def _init(self):
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self.local_evaluator = self.make_local_evaluator(
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self.env_creator, self._policy_graph)
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self.remote_evaluators = self.make_remote_evaluators(
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self.env_creator, self._policy_graph, self.config["num_workers"],
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{})
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self.optimizer = SyncSamplesOptimizer(self.local_evaluator,
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self.remote_evaluators,
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self.config["optimizer"])
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def _train(self):
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prev_steps = self.optimizer.num_steps_sampled
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self.optimizer.step()
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result = self.optimizer.collect_metrics(
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self.config["collect_metrics_timeout"])
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result.update(timesteps_this_iter=self.optimizer.num_steps_sampled -
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prev_steps)
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return result
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