Tianshou/tianshou/highlevel/agent.py

import os
from abc import ABC, abstractmethod
from collections.abc import Callable
from dataclasses import dataclass

import torch

from tianshou.config import PGConfig, PPOConfig, RLAgentConfig, RLSamplingConfig
from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
from tianshou.exploration import BaseNoise
from tianshou.highlevel.env import Environments
from tianshou.highlevel.logger import Logger
from tianshou.highlevel.module import ActorFactory, CriticFactory, TDevice
from tianshou.highlevel.optim import LRSchedulerFactory, OptimizerFactory
from tianshou.policy import BasePolicy, PPOPolicy, SACPolicy
from tianshou.trainer import BaseTrainer, OffpolicyTrainer, OnpolicyTrainer
from tianshou.utils.net.common import ActorCritic

CHECKPOINT_DICT_KEY_MODEL = "model"
CHECKPOINT_DICT_KEY_OBS_RMS = "obs_rms"


class AgentFactory(ABC):
    def __init__(self, sampling_config: RLSamplingConfig):
        self.sampling_config = sampling_config

    def create_train_test_collector(self, policy: BasePolicy, envs: Environments):
        buffer_size = self.sampling_config.buffer_size
        train_envs = envs.train_envs
        if len(train_envs) > 1:
            buffer = VectorReplayBuffer(buffer_size, len(train_envs))
        else:
            buffer = ReplayBuffer(buffer_size)
        train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
        test_collector = Collector(policy, envs.test_envs)
        return train_collector, test_collector

    @abstractmethod
    def create_policy(self, envs: Environments, device: TDevice) -> BasePolicy:
        pass

    @staticmethod
    def _create_save_best_fn(envs: Environments, log_path: str) -> Callable:
        def save_best_fn(pol: torch.nn.Module) -> None:
            state = {
                CHECKPOINT_DICT_KEY_MODEL: pol.state_dict(),
                CHECKPOINT_DICT_KEY_OBS_RMS: envs.train_envs.get_obs_rms(),
            }
            torch.save(state, os.path.join(log_path, "policy.pth"))

        return save_best_fn

    @staticmethod
    def load_checkpoint(policy: torch.nn.Module, path, envs: Environments, device: TDevice):
        ckpt = torch.load(path, map_location=device)
        policy.load_state_dict(ckpt[CHECKPOINT_DICT_KEY_MODEL])
        if envs.train_envs:
            envs.train_envs.set_obs_rms(ckpt[CHECKPOINT_DICT_KEY_OBS_RMS])
        if envs.test_envs:
            envs.test_envs.set_obs_rms(ckpt[CHECKPOINT_DICT_KEY_OBS_RMS])
        print("Loaded agent and obs. running means from: ", path)  # TODO logging

    @abstractmethod
    def create_trainer(
        self,
        policy: BasePolicy,
        train_collector: Collector,
        test_collector: Collector,
        envs: Environments,
        logger: Logger,
    ) -> BaseTrainer:
        pass


class OnpolicyAgentFactory(AgentFactory, ABC):
    def create_trainer(
        self,
        policy: BasePolicy,
        train_collector: Collector,
        test_collector: Collector,
        envs: Environments,
        logger: Logger,
    ) -> OnpolicyTrainer:
        sampling_config = self.sampling_config
        return OnpolicyTrainer(
            policy=policy,
            train_collector=train_collector,
            test_collector=test_collector,
            max_epoch=sampling_config.num_epochs,
            step_per_epoch=sampling_config.step_per_epoch,
            repeat_per_collect=sampling_config.repeat_per_collect,
            episode_per_test=sampling_config.num_test_envs,
            batch_size=sampling_config.batch_size,
            step_per_collect=sampling_config.step_per_collect,
            save_best_fn=self._create_save_best_fn(envs, logger.log_path),
            logger=logger.logger,
            test_in_train=False,
        )


class OffpolicyAgentFactory(AgentFactory, ABC):
    def create_trainer(
        self,
        policy: BasePolicy,
        train_collector: Collector,
        test_collector: Collector,
        envs: Environments,
        logger: Logger,
    ) -> OffpolicyTrainer:
        sampling_config = self.sampling_config
        return OffpolicyTrainer(
            policy=policy,
            train_collector=train_collector,
            test_collector=test_collector,
            max_epoch=sampling_config.num_epochs,
            step_per_epoch=sampling_config.step_per_epoch,
            step_per_collect=sampling_config.step_per_collect,
            episode_per_test=sampling_config.num_test_envs,
            batch_size=sampling_config.batch_size,
            save_best_fn=self._create_save_best_fn(envs, logger.log_path),
            logger=logger.logger,
            update_per_step=sampling_config.update_per_step,
            test_in_train=False,
        )


class PPOAgentFactory(OnpolicyAgentFactory):
    def __init__(
        self,
        general_config: RLAgentConfig,
        pg_config: PGConfig,
        ppo_config: PPOConfig,
        sampling_config: RLSamplingConfig,
        actor_factory: ActorFactory,
        critic_factory: CriticFactory,
        optimizer_factory: OptimizerFactory,
        dist_fn,
        lr: float,
        lr_scheduler_factory: LRSchedulerFactory | None = None,
    ):
        super().__init__(sampling_config)
        self.optimizer_factory = optimizer_factory
        self.critic_factory = critic_factory
        self.actor_factory = actor_factory
        self.ppo_config = ppo_config
        self.pg_config = pg_config
        self.general_config = general_config
        self.lr = lr
        self.lr_scheduler_factory = lr_scheduler_factory
        self.dist_fn = dist_fn

    def create_policy(self, envs: Environments, device: TDevice) -> PPOPolicy:
        actor = self.actor_factory.create_module(envs, device)
        critic = self.critic_factory.create_module(envs, device, use_action=False)
        actor_critic = ActorCritic(actor, critic)
        optim = self.optimizer_factory.create_optimizer(actor_critic, self.lr)
        if self.lr_scheduler_factory is not None:
            lr_scheduler = self.lr_scheduler_factory.create_scheduler(optim)
        else:
            lr_scheduler = None
        return PPOPolicy(
            # nn-stuff
            actor,
            critic,
            optim,
            dist_fn=self.dist_fn,
            lr_scheduler=lr_scheduler,
            # env-stuff
            action_space=envs.get_action_space(),
            action_scaling=True,
            # general_config
            discount_factor=self.general_config.gamma,
            gae_lambda=self.general_config.gae_lambda,
            reward_normalization=self.general_config.rew_norm,
            action_bound_method=self.general_config.action_bound_method,
            # pg_config
            max_grad_norm=self.pg_config.max_grad_norm,
            vf_coef=self.pg_config.vf_coef,
            ent_coef=self.pg_config.ent_coef,
            # ppo_config
            eps_clip=self.ppo_config.eps_clip,
            value_clip=self.ppo_config.value_clip,
            dual_clip=self.ppo_config.dual_clip,
            advantage_normalization=self.ppo_config.norm_adv,
            recompute_advantage=self.ppo_config.recompute_adv,
        )


class SACAgentFactory(OffpolicyAgentFactory):
    def __init__(
        self,
        config: "SACAgentFactory.Config",
        sampling_config: RLSamplingConfig,
        actor_factory: ActorFactory,
        critic1_factory: CriticFactory,
        critic2_factory: CriticFactory,
        optim_factory: OptimizerFactory,
        exploration_noise: BaseNoise | None = None,
    ):
        super().__init__(sampling_config)
        self.critic2_factory = critic2_factory
        self.critic1_factory = critic1_factory
        self.actor_factory = actor_factory
        self.exploration_noise = exploration_noise
        self.optim_factory = optim_factory
        self.config = config

    def create_policy(self, envs: Environments, device: TDevice) -> BasePolicy:
        actor = self.actor_factory.create_module(envs, device)
        critic1 = self.critic1_factory.create_module(envs, device, use_action=True)
        critic2 = self.critic2_factory.create_module(envs, device, use_action=True)
        actor_optim = self.optim_factory.create_optimizer(actor, lr=self.config.actor_lr)
        critic1_optim = self.optim_factory.create_optimizer(critic1, lr=self.config.critic1_lr)
        critic2_optim = self.optim_factory.create_optimizer(critic2, lr=self.config.critic2_lr)
        return SACPolicy(
            actor,
            actor_optim,
            critic1,
            critic1_optim,
            critic2,
            critic2_optim,
            tau=self.config.tau,
            gamma=self.config.gamma,
            alpha=self.config.alpha,
            estimation_step=self.config.estimation_step,
            action_space=envs.get_action_space(),
            deterministic_eval=self.config.deterministic_eval,
            exploration_noise=self.exploration_noise,
        )

    @dataclass
    class Config:
        """SAC configuration."""

        tau: float = 0.005
        gamma: float = 0.99
        alpha: float | tuple[float, torch.Tensor, torch.optim.Optimizer] = 0.2
        reward_normalization: bool = False
        estimation_step: int = 1
        deterministic_eval: bool = True
        actor_lr: float = 1e-3
        critic1_lr: float = 1e-3
        critic2_lr: float = 1e-3