109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
#!/usr/bin/env python3
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import datetime
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import os
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from collections.abc import Sequence
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from typing import Literal
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from jsonargparse import CLI
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from torch.distributions import Independent, Normal
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from examples.mujoco.mujoco_env import MujocoEnvFactory
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from tianshou.highlevel.agent import PPOAgentFactory, PPOConfig
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from tianshou.highlevel.config import RLSamplingConfig
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from tianshou.highlevel.experiment import (
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RLExperiment,
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RLExperimentConfig,
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)
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from tianshou.highlevel.logger import DefaultLoggerFactory
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from tianshou.highlevel.module import (
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ContinuousActorProbFactory,
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ContinuousNetCriticFactory,
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)
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from tianshou.highlevel.optim import AdamOptimizerFactory, LinearLRSchedulerFactory
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def main(
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experiment_config: RLExperimentConfig,
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task: str = "Ant-v4",
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buffer_size: int = 4096,
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hidden_sizes: Sequence[int] = (64, 64),
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lr: float = 3e-4,
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gamma: float = 0.99,
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epoch: int = 100,
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step_per_epoch: int = 30000,
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step_per_collect: int = 2048,
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repeat_per_collect: int = 10,
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batch_size: int = 64,
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training_num: int = 64,
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test_num: int = 10,
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rew_norm: bool = True,
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vf_coef: float = 0.25,
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ent_coef: float = 0.0,
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gae_lambda: float = 0.95,
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bound_action_method: Literal["clip", "tanh"] | None = "clip",
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lr_decay: bool = True,
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max_grad_norm: float = 0.5,
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eps_clip: float = 0.2,
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dual_clip: float | None = None,
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value_clip: bool = False,
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norm_adv: bool = False,
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recompute_adv: bool = True,
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):
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now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
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log_name = os.path.join(task, "ppo", str(experiment_config.seed), now)
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logger_factory = DefaultLoggerFactory()
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sampling_config = RLSamplingConfig(
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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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repeat_per_collect=repeat_per_collect,
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)
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env_factory = MujocoEnvFactory(task, experiment_config.seed, sampling_config)
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def dist_fn(*logits):
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return Independent(Normal(*logits), 1)
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ppo_config = PPOConfig(
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gamma=gamma,
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gae_lambda=gae_lambda,
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action_bound_method=bound_action_method,
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rew_norm=rew_norm,
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ent_coef=ent_coef,
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vf_coef=vf_coef,
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max_grad_norm=max_grad_norm,
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value_clip=value_clip,
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norm_adv=norm_adv,
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eps_clip=eps_clip,
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dual_clip=dual_clip,
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recompute_adv=recompute_adv,
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)
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actor_factory = ContinuousActorProbFactory(hidden_sizes)
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critic_factory = ContinuousNetCriticFactory(hidden_sizes)
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optim_factory = AdamOptimizerFactory()
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lr_scheduler_factory = LinearLRSchedulerFactory(sampling_config) if lr_decay else None
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agent_factory = PPOAgentFactory(
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ppo_config,
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sampling_config,
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actor_factory,
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critic_factory,
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optim_factory,
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dist_fn,
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lr,
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lr_scheduler_factory,
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)
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experiment = RLExperiment(experiment_config, env_factory, logger_factory, agent_factory)
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experiment.run(log_name)
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if __name__ == "__main__":
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CLI(main)
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