Tianshou/examples/mujoco/mujoco_ddpg_hl.py
2023-10-18 20:44:16 +02:00

79 lines
2.2 KiB
Python

#!/usr/bin/env python3
import datetime
import os
from collections.abc import Sequence
from jsonargparse import CLI
from examples.mujoco.mujoco_env import MujocoEnvFactory
from tianshou.highlevel.config import RLSamplingConfig
from tianshou.highlevel.experiment import (
DDPGExperimentBuilder,
RLExperimentConfig,
)
from tianshou.highlevel.params.noise import MaxActionScaledGaussian
from tianshou.highlevel.params.policy_params import DDPGParams
def main(
experiment_config: RLExperimentConfig,
task: str = "Ant-v3",
buffer_size: int = 1000000,
hidden_sizes: Sequence[int] = (256, 256),
actor_lr: float = 1e-3,
critic_lr: float = 1e-3,
gamma: float = 0.99,
tau: float = 0.005,
exploration_noise: float = 0.1,
start_timesteps: int = 25000,
epoch: int = 200,
step_per_epoch: int = 5000,
step_per_collect: int = 1,
update_per_step: int = 1,
n_step: int = 1,
batch_size: int = 256,
training_num: int = 1,
test_num: int = 10,
):
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
log_name = os.path.join(task, "ppo", str(experiment_config.seed), now)
sampling_config = RLSamplingConfig(
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,
start_timesteps=start_timesteps,
start_timesteps_random=True,
)
env_factory = MujocoEnvFactory(task, experiment_config.seed, sampling_config)
experiment = (
DDPGExperimentBuilder(experiment_config, env_factory, sampling_config)
.with_ddpg_params(
DDPGParams(
actor_lr=actor_lr,
critic_lr=critic_lr,
gamma=gamma,
tau=tau,
exploration_noise=MaxActionScaledGaussian(exploration_noise),
estimation_step=n_step,
),
)
.with_actor_factory_default(hidden_sizes)
.with_critic_factory_default(hidden_sizes)
.build()
)
experiment.run(log_name)
if __name__ == "__main__":
CLI(main)