Tianshou/examples/mujoco/mujoco_ppo_hl.py

61 lines
2.0 KiB
Python

#!/usr/bin/env python3
import datetime
import os
from jsonargparse import CLI
from torch.distributions import Independent, Normal
from examples.mujoco.mujoco_env import MujocoEnvFactory
from tianshou.config import (
BasicExperimentConfig,
LoggerConfig,
NNConfig,
PGConfig,
PPOConfig,
RLAgentConfig,
RLSamplingConfig,
)
from tianshou.highlevel.agent import PPOAgentFactory
from tianshou.highlevel.logger import DefaultLoggerFactory
from tianshou.highlevel.module import ContinuousActorProbFactory, ContinuousNetCriticFactory
from tianshou.highlevel.optim import AdamOptimizerFactory, LinearLRSchedulerFactory
from tianshou.highlevel.experiment import RLExperiment
def main(
experiment_config: BasicExperimentConfig,
logger_config: LoggerConfig,
sampling_config: RLSamplingConfig,
general_config: RLAgentConfig,
pg_config: PGConfig,
ppo_config: PPOConfig,
nn_config: NNConfig,
):
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
log_name = os.path.join(experiment_config.task, "ppo", str(experiment_config.seed), now)
logger_factory = DefaultLoggerFactory(logger_config)
env_factory = MujocoEnvFactory(experiment_config, sampling_config)
def dist_fn(*logits):
return Independent(Normal(*logits), 1)
actor_factory = ContinuousActorProbFactory(nn_config.hidden_sizes)
critic_factory = ContinuousNetCriticFactory(nn_config.hidden_sizes)
optim_factory = AdamOptimizerFactory(lr=nn_config.lr)
lr_scheduler_factory = LinearLRSchedulerFactory(nn_config, sampling_config)
agent_factory = PPOAgentFactory(general_config, pg_config, ppo_config, sampling_config, nn_config,
actor_factory, critic_factory, optim_factory, dist_fn, lr_scheduler_factory)
experiment = RLExperiment(experiment_config, logger_config, general_config, sampling_config,
env_factory,
logger_factory,
agent_factory)
experiment.run(log_name)
if __name__ == "__main__":
CLI(main)