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Sam ToyerandEric Liang
663e92ab3f
* [rllib] Separate optimisers for DDPG actor & crit. * [rllib] Better names for DDPG variables & options Config changes: - noise_scale -> exploration_ou_noise_scale - exploration_theta -> exploration_ou_theta - exploration_sigma -> exploration_ou_sigma - act_noise -> exploration_gaussian_sigma - noise_clip -> target_noise_clip * [rllib] Make DDPG less class-y Used functions to replace three classes with only an __init__ method & a handful of unrelated attributes. * [rllib] Refactor DDPG noise * [rllib] Unify DDPG exploration annealing Added option "exploration_should_anneal" to enable linear annealing of exploration noise. By default this is off, for consistency with DDPG & TD3 papers. Also renamed "exploration_final_eps" to "exploration_final_scale" (that name seems to have been carried over from DQN, and doesn't really make sense here). Finally, tried to rename "eps" to "noise_scale" wherever possible.
58 lines
1.8 KiB
Python
58 lines
1.8 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.ddpg.ddpg import DDPGTrainer, \
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DEFAULT_CONFIG as DDPG_CONFIG
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from ray.rllib.utils import merge_dicts
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TD3_DEFAULT_CONFIG = merge_dicts(
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DDPG_CONFIG,
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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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# other changes & things we want to keep fixed: IID Gaussian
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# exploration noise, larger actor learning rate, no l2 regularisation,
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# no Huber loss, etc.
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"exploration_should_anneal": False,
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"exploration_noise_type": "gaussian",
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"exploration_gaussian_sigma": 0.1,
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"learning_starts": 10000,
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"pure_exploration_steps": 10000,
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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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"target_network_update_freq": 0,
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"optimizer_class": "SyncReplayOptimizer",
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"num_workers": 0,
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"num_gpus_per_worker": 0,
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"per_worker_exploration": False,
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"worker_side_prioritization": False,
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"buffer_size": 1000000,
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"prioritized_replay": False,
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"clip_rewards": False,
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"use_state_preprocessor": False,
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},
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)
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class TD3Trainer(DDPGTrainer):
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"""A more stable successor to TD3. By default, this uses a near-identical
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configuration to that reported in the TD3 paper."""
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_name = "TD3"
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_default_config = TD3_DEFAULT_CONFIG
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