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61 lines
2.5 KiB
Python
61 lines
2.5 KiB
Python
from ray.rllib.agents.dqn.apex import ApexTrainer
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from ray.rllib.agents.ddpg.ddpg import DDPGTrainer, DEFAULT_CONFIG as DDPG_CONFIG
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from ray.rllib.evaluation.worker_set import WorkerSet
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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.util.iter import LocalIterator
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APEX_DDPG_DEFAULT_CONFIG = DDPGTrainer.merge_trainer_configs(
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DDPG_CONFIG, # see also the options in ddpg.py, which are also supported
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{
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"optimizer": {
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"max_weight_sync_delay": 400,
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"num_replay_buffer_shards": 4,
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"debug": False,
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},
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"exploration_config": {"type": "PerWorkerOrnsteinUhlenbeckNoise"},
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"n_step": 3,
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"num_gpus": 0,
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"num_workers": 32,
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"buffer_size": 2000000,
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# TODO(jungong) : update once Apex supports replay_buffer_config.
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"no_local_replay_buffer": True,
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# Whether all shards of the replay buffer must be co-located
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# with the learner process (running the execution plan).
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# This is preferred b/c the learner process should have quick
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# access to the data from the buffer shards, avoiding network
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# traffic each time samples from the buffer(s) are drawn.
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# Set this to False for relaxing this constraint and allowing
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# replay shards to be created on node(s) other than the one
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# on which the learner is located.
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"replay_buffer_shards_colocated_with_driver": True,
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"learning_starts": 50000,
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"train_batch_size": 512,
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"rollout_fragment_length": 50,
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"target_network_update_freq": 500000,
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"min_sample_timesteps_per_reporting": 25000,
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"worker_side_prioritization": True,
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"min_time_s_per_reporting": 30,
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# Experimental flag.
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# If True, the execution plan API will not be used. Instead,
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# a Trainer's `training_iteration` method will be called as-is each
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# training iteration.
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"_disable_execution_plan_api": False,
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},
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_allow_unknown_configs=True,
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)
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class ApexDDPGTrainer(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 APEX_DDPG_DEFAULT_CONFIG
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@staticmethod
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@override(DDPGTrainer)
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def execution_plan(
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workers: WorkerSet, config: dict, **kwargs
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) -> LocalIterator[dict]:
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"""Use APEX-DQN's execution plan."""
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return ApexTrainer.execution_plan(workers, config, **kwargs)
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