mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 09:29:05 +08:00
Eric Liang
40 lines
1.2 KiB
Python
40 lines
1.2 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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# Note: do not introduce unnecessary library dependencies here, e.g. gym.
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# This file is imported from the tune module in order to register RLlib agents.
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from ray.tune.registry import register_trainable
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from ray.rllib.evaluation.policy_graph import PolicyGraph
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from ray.rllib.evaluation.tf_policy_graph import TFPolicyGraph
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from ray.rllib.env.async_vector_env import AsyncVectorEnv
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from ray.rllib.env.multi_agent_env import MultiAgentEnv
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from ray.rllib.env.vector_env import VectorEnv
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from ray.rllib.env.serving_env import ServingEnv
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from ray.rllib.evaluation.policy_evaluator import PolicyEvaluator
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from ray.rllib.evaluation.sample_batch import SampleBatch
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def _register_all():
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for key in [
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"PPO", "ES", "DQN", "APEX", "A3C", "BC", "PG", "DDPG", "APEX_DDPG",
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"__fake", "__sigmoid_fake_data", "__parameter_tuning"
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]:
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from ray.rllib.agents.agent import get_agent_class
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register_trainable(key, get_agent_class(key))
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_register_all()
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__all__ = [
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"PolicyGraph",
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"TFPolicyGraph",
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"PolicyEvaluator",
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"SampleBatch",
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"AsyncVectorEnv",
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"MultiAgentEnv",
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"VectorEnv",
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"ServingEnv",
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]
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