mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 12:10:40 +08:00
andrewztan
1475600c81
* removed ddpg2 * removed ddpg2 from codebase * added tests used in ddpg vs ddpg2 comparison * added notes about training timesteps to yaml files * removed ddpg2 yaml files * removed unnecessary configs from yaml files * removed unnecessary configs from yaml files * moved pendulum, mountaincarcontinuous, and halfcheetah tests to tuned_examples * moved pendulum, mountaincarcontinuous, and halfcheetah tests to tuned_examples * added more configuration details to yaml files * removed random starts from halfcheetah
28 lines
966 B
Python
28 lines
966 B
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.utils.policy_graph import PolicyGraph
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from ray.rllib.utils.tf_policy_graph import TFPolicyGraph
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from ray.rllib.utils.common_policy_evaluator import CommonPolicyEvaluator
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from ray.rllib.optimizers.sample_batch import SampleBatch
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def _register_all():
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for key in ["PPO", "ES", "DQN", "APEX", "A3C", "BC", "PG", "DDPG",
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"APEX_DDPG", "__fake", "__sigmoid_fake_data",
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"__parameter_tuning"]:
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from ray.rllib.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", "TFPolicyGraph", "CommonPolicyEvaluator", "SampleBatch"
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]
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