6cbee188b8
of number of environments in SamplingConfig is used (values are now passed to factory method) This is clearer and removes the need to pass otherwise unnecessary configuration to environment factories at construction
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
#!/usr/bin/env python3
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import os
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from collections.abc import Sequence
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from jsonargparse import CLI
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from examples.atari.atari_callbacks import (
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TestEpochCallbackDQNSetEps,
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TrainEpochCallbackNatureDQNEpsLinearDecay,
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)
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from examples.atari.atari_network import (
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IntermediateModuleFactoryAtariDQN,
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)
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from examples.atari.atari_wrapper import AtariEnvFactory, AtariStopCallback
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from tianshou.highlevel.config import SamplingConfig
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from tianshou.highlevel.experiment import (
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ExperimentConfig,
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IQNExperimentBuilder,
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)
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from tianshou.highlevel.params.policy_params import IQNParams
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from tianshou.utils import logging
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from tianshou.utils.logging import datetime_tag
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def main(
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experiment_config: ExperimentConfig,
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task: str = "PongNoFrameskip-v4",
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scale_obs: int = 0,
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eps_test: float = 0.005,
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eps_train: float = 1.0,
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eps_train_final: float = 0.05,
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buffer_size: int = 100000,
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lr: float = 0.0001,
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gamma: float = 0.99,
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sample_size: int = 32,
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online_sample_size: int = 8,
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target_sample_size: int = 8,
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num_cosines: int = 64,
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hidden_sizes: Sequence[int] = (512,),
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n_step: int = 3,
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target_update_freq: int = 500,
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epoch: int = 100,
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step_per_epoch: int = 100000,
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step_per_collect: int = 10,
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update_per_step: float = 0.1,
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batch_size: int = 32,
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training_num: int = 10,
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test_num: int = 10,
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frames_stack: int = 4,
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save_buffer_name: str | None = None, # TODO support?
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):
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log_name = os.path.join(task, "iqn", str(experiment_config.seed), datetime_tag())
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sampling_config = SamplingConfig(
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num_epochs=epoch,
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step_per_epoch=step_per_epoch,
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batch_size=batch_size,
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num_train_envs=training_num,
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num_test_envs=test_num,
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buffer_size=buffer_size,
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step_per_collect=step_per_collect,
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update_per_step=update_per_step,
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repeat_per_collect=None,
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replay_buffer_stack_num=frames_stack,
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replay_buffer_ignore_obs_next=True,
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replay_buffer_save_only_last_obs=True,
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)
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env_factory = AtariEnvFactory(task, experiment_config.seed, frames_stack, scale=scale_obs)
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experiment = (
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IQNExperimentBuilder(env_factory, experiment_config, sampling_config)
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.with_iqn_params(
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IQNParams(
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discount_factor=gamma,
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estimation_step=n_step,
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lr=lr,
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sample_size=sample_size,
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online_sample_size=online_sample_size,
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target_update_freq=target_update_freq,
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target_sample_size=target_sample_size,
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hidden_sizes=hidden_sizes,
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num_cosines=num_cosines,
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),
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)
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.with_preprocess_network_factory(IntermediateModuleFactoryAtariDQN(features_only=True))
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.with_trainer_epoch_callback_train(
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TrainEpochCallbackNatureDQNEpsLinearDecay(eps_train, eps_train_final),
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)
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.with_trainer_epoch_callback_test(TestEpochCallbackDQNSetEps(eps_test))
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.with_trainer_stop_callback(AtariStopCallback(task))
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.build()
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)
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experiment.run(log_name)
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if __name__ == "__main__":
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logging.run_main(lambda: CLI(main))
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