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#!/usr/bin/env python3
import os
from collections.abc import Sequence
from examples.atari.atari_network import (
IntermediateModuleFactoryAtariDQN,
)
from examples.atari.atari_wrapper import AtariEnvFactory, AtariEpochStopCallback
from tianshou.highlevel.config import SamplingConfig
from tianshou.highlevel.experiment import (
ExperimentConfig,
IQNExperimentBuilder,
)
from tianshou.highlevel.params.policy_params import IQNParams
from tianshou.highlevel.trainer import (
EpochTestCallbackDQNSetEps,
EpochTrainCallbackDQNEpsLinearDecay,
)
from tianshou.utils import logging
from tianshou.utils.logging import datetime_tag
def main(
experiment_config: ExperimentConfig,
task: str = "PongNoFrameskip-v4",
scale_obs: bool = False,
eps_test: float = 0.005,
eps_train: float = 1.0,
eps_train_final: float = 0.05,
buffer_size: int = 100000,
lr: float = 0.0001,
gamma: float = 0.99,
sample_size: int = 32,
online_sample_size: int = 8,
target_sample_size: int = 8,
num_cosines: int = 64,
hidden_sizes: Sequence[int] = (512,),
n_step: int = 3,
target_update_freq: int = 500,
epoch: int = 100,
step_per_epoch: int = 100000,
step_per_collect: int = 10,
update_per_step: float = 0.1,
batch_size: int = 32,
training_num: int = 10,
test_num: int = 10,
frames_stack: int = 4,
save_buffer_name: str | None = None, # TODO support?
) -> None:
log_name = os.path.join(task, "iqn", str(experiment_config.seed), datetime_tag())
sampling_config = SamplingConfig(
num_epochs=epoch,
step_per_epoch=step_per_epoch,
batch_size=batch_size,
num_train_envs=training_num,
num_test_envs=test_num,
buffer_size=buffer_size,
step_per_collect=step_per_collect,
update_per_step=update_per_step,
repeat_per_collect=None,
replay_buffer_stack_num=frames_stack,
replay_buffer_ignore_obs_next=True,
replay_buffer_save_only_last_obs=True,
)
env_factory = AtariEnvFactory(
task,
sampling_config.train_seed,
sampling_config.test_seed,
frames_stack,
scale=scale_obs,
)
experiment = (
IQNExperimentBuilder(env_factory, experiment_config, sampling_config)
.with_iqn_params(
IQNParams(
discount_factor=gamma,
estimation_step=n_step,
lr=lr,
sample_size=sample_size,
online_sample_size=online_sample_size,
target_update_freq=target_update_freq,
target_sample_size=target_sample_size,
hidden_sizes=hidden_sizes,
num_cosines=num_cosines,
),
)
.with_preprocess_network_factory(IntermediateModuleFactoryAtariDQN(features_only=True))
.with_epoch_train_callback(
EpochTrainCallbackDQNEpsLinearDecay(eps_train, eps_train_final),
)
.with_epoch_test_callback(EpochTestCallbackDQNSetEps(eps_test))
.with_epoch_stop_callback(AtariEpochStopCallback(task))
.build()
)
experiment.run(run_name=log_name)
if __name__ == "__main__":
logging.run_cli(main)
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