mirror of
https://github.com/vale981/ray
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122 lines
4.3 KiB
Python
122 lines
4.3 KiB
Python
"""A more stable successor to TD3.
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By default, this uses a near-identical configuration to that reported in the
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TD3 paper.
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"""
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from ray.rllib.algorithms.ddpg.ddpg import DDPG, DDPGConfig
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.deprecation import Deprecated
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from ray.rllib.utils.typing import AlgorithmConfigDict
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from ray.rllib.utils.deprecation import DEPRECATED_VALUE
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class TD3Config(DDPGConfig):
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"""Defines a configuration class from which a TD3 Algorithm can be built.
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Example:
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>>> from ray.rllib.algorithms.ddpg.td3 import TD3Config
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>>> config = TD3Config().training(lr=0.01).resources(num_gpus=1)
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>>> print(config.to_dict())
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>>> # Build a Algorithm object from the config and run one training iteration.
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>>> algo = config.build(env="Pendulum-v1")
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>>> algo.train()
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Example:
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>>> from ray.rllib.algorithms.ddpg.td3 import TD3Config
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>>> from ray import tune
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>>> config = TD3Config()
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>>> # Print out some default values.
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>>> print(config.lr)
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0.0004
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>>> # Update the config object.
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>>> config.training(lr=tune.grid_search([0.001, 0.0001]))
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>>> # Set the config object's env.
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>>> config.environment(env="Pendulum-v1")
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>>> # Use to_dict() to get the old-style python config dict
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>>> # when running with tune.
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>>> tune.run(
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... "TD3",
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... stop={"episode_reward_mean": 200},
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... config=config.to_dict(),
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... )
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"""
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def __init__(self, algo_class=None):
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"""Initializes a TD3Config instance."""
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super().__init__(algo_class=algo_class or TD3)
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# fmt: off
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# __sphinx_doc_begin__
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# Override some of DDPG/SimpleQ/Algorithm's default values with TD3-specific
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# values.
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# .training()
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# largest changes: twin Q functions, delayed policy updates, target
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# smoothing, no l2-regularization.
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self.twin_q = True
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self.policy_delay = 2
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self.smooth_target_policy = True,
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self.l2_reg = 0.0
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# Different tau (affecting target network update).
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self.tau = 5e-3
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# Different batch size.
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self.train_batch_size = 100
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# No prioritized replay by default (we may want to change this at some
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# point).
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self.replay_buffer_config = {
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"type": "MultiAgentReplayBuffer",
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# Specify prioritized replay by supplying a buffer type that supports
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# prioritization, for example: MultiAgentPrioritizedReplayBuffer.
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"prioritized_replay": DEPRECATED_VALUE,
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"capacity": 1000000,
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"learning_starts": 10000,
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"worker_side_prioritization": False,
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}
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# .exploration()
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# TD3 uses Gaussian Noise by default.
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self.exploration_config = {
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# TD3 uses simple Gaussian noise on top of deterministic NN-output
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# actions (after a possible pure random phase of n timesteps).
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"type": "GaussianNoise",
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# For how many timesteps should we return completely random
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# actions, before we start adding (scaled) noise?
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"random_timesteps": 10000,
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# Gaussian stddev of action noise for exploration.
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"stddev": 0.1,
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# Scaling settings by which the Gaussian noise is scaled before
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# being added to the actions. NOTE: The scale timesteps start only
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# after(!) any random steps have been finished.
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# By default, do not anneal over time (fixed 1.0).
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"initial_scale": 1.0,
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"final_scale": 1.0,
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"scale_timesteps": 1,
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}
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# __sphinx_doc_end__
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# fmt: on
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class TD3(DDPG):
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@classmethod
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@override(DDPG)
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def get_default_config(cls) -> AlgorithmConfigDict:
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return TD3Config().to_dict()
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# Deprecated: Use ray.rllib.algorithms.ddpg..td3.TD3Config instead!
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class _deprecated_default_config(dict):
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def __init__(self):
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super().__init__(TD3Config().to_dict())
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@Deprecated(
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old="ray.rllib.algorithms.ddpg.td3::TD3_DEFAULT_CONFIG",
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new="ray.rllib.algorithms.td3.td3::TD3Config(...)",
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error=False,
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)
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def __getitem__(self, item):
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return super().__getitem__(item)
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TD3_DEFAULT_CONFIG = _deprecated_default_config()
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