mirror of
https://github.com/vale981/ray
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37 lines
1.2 KiB
Python
37 lines
1.2 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.dqn.apex import APEX_TRAINER_PROPERTIES
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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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APEX_DDPG_DEFAULT_CONFIG = merge_dicts(
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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": merge_dicts(
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DDPG_CONFIG["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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"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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"learning_starts": 50000,
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"train_batch_size": 512,
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"sample_batch_size": 50,
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"target_network_update_freq": 500000,
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"timesteps_per_iteration": 25000,
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"per_worker_exploration": True,
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"worker_side_prioritization": True,
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"min_iter_time_s": 30,
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},
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)
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ApexDDPGTrainer = DDPGTrainer.with_updates(
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name="APEX_DDPG",
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default_config=APEX_DDPG_DEFAULT_CONFIG,
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**APEX_TRAINER_PROPERTIES)
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