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https://github.com/vale981/ray
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73 lines
2.8 KiB
Python
73 lines
2.8 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 DDPGConfig, DDPGTrainer
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.typing import TrainerConfigDict
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from ray.rllib.utils.deprecation import DEPRECATED_VALUE
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TD3_DEFAULT_CONFIG = DDPGTrainer.merge_trainer_configs(
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DDPGConfig().to_dict(),
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{
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# largest changes: twin Q functions, delayed policy updates, and target
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# smoothing
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"twin_q": True,
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"policy_delay": 2,
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"smooth_target_policy": True,
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"target_noise": 0.2,
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"target_noise_clip": 0.5,
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"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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# other changes & things we want to keep fixed:
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# larger actor learning rate, no l2 regularisation, no Huber loss, etc.
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"actor_hiddens": [400, 300],
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"critic_hiddens": [400, 300],
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"n_step": 1,
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"gamma": 0.99,
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"actor_lr": 1e-3,
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"critic_lr": 1e-3,
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"l2_reg": 0.0,
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"tau": 5e-3,
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"train_batch_size": 100,
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"use_huber": False,
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# Update the target network every `target_network_update_freq` sample timesteps.
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"target_network_update_freq": 0,
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"num_workers": 0,
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"num_gpus_per_worker": 0,
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"clip_rewards": False,
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"use_state_preprocessor": False,
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"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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},
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)
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class TD3Trainer(DDPGTrainer):
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@classmethod
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@override(DDPGTrainer)
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def get_default_config(cls) -> TrainerConfigDict:
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return TD3_DEFAULT_CONFIG
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