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#!/usr/bin/env python3
import os
from examples.atari.atari_network import (
IntermediateModuleFactoryAtariDQN,
IntermediateModuleFactoryAtariDQNFeatures,
)
from examples.atari.atari_wrapper import AtariEnvFactory, AtariEpochStopCallback
from tianshou.highlevel.config import SamplingConfig
from tianshou.highlevel.experiment import (
DQNExperimentBuilder,
ExperimentConfig,
)
from tianshou.highlevel.params.policy_params import DQNParams
from tianshou.highlevel.params.policy_wrapper import (
PolicyWrapperFactoryIntrinsicCuriosity,
)
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,
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?
icm_lr_scale: float = 0.0,
icm_reward_scale: float = 0.01,
icm_forward_loss_weight: float = 0.2,
) -> None:
log_name = os.path.join(task, "dqn", 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,
)
builder = (
DQNExperimentBuilder(env_factory, experiment_config, sampling_config)
.with_dqn_params(
DQNParams(
discount_factor=gamma,
estimation_step=n_step,
lr=lr,
target_update_freq=target_update_freq,
),
)
.with_model_factory(IntermediateModuleFactoryAtariDQN())
.with_epoch_train_callback(
EpochTrainCallbackDQNEpsLinearDecay(eps_train, eps_train_final),
)
.with_epoch_test_callback(EpochTestCallbackDQNSetEps(eps_test))
.with_epoch_stop_callback(AtariEpochStopCallback(task))
)
if icm_lr_scale > 0:
builder.with_policy_wrapper_factory(
PolicyWrapperFactoryIntrinsicCuriosity(
feature_net_factory=IntermediateModuleFactoryAtariDQNFeatures(),
hidden_sizes=[512],
lr=lr,
lr_scale=icm_lr_scale,
reward_scale=icm_reward_scale,
forward_loss_weight=icm_forward_loss_weight,
),
)
experiment = builder.build()
experiment.run(run_name=log_name)
if __name__ == "__main__":
logging.run_cli(main)
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