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d0fab84e4d
The DDPG/TD3 algorithms currently do not have a PyTorch implementation. This PR adds PyTorch support for DDPG/TD3 to RLlib. This PR: - Depends on the re-factor PR for DDPG (Functional Algorithm API). - Adds learning regression tests for the PyTorch version of DDPG and a DDPG (torch) - Updates the documentation to reflect that DDPG and TD3 now support PyTorch. * Learning Pendulum-v0 on torch version (same config as tf). Wall time a little slower (~20% than tf). * Fix GPU target model problem.
63 lines
2.2 KiB
Python
63 lines
2.2 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.agents.ddpg.ddpg import DDPGTrainer, \
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DEFAULT_CONFIG as DDPG_CONFIG
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TD3_DEFAULT_CONFIG = DDPGTrainer.merge_trainer_configs(
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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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"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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"learning_starts": 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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"num_workers": 0,
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"num_gpus_per_worker": 0,
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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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TD3Trainer = DDPGTrainer.with_updates(
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name="TD3",
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default_config=TD3_DEFAULT_CONFIG,
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)
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