367778d37f
Policy objects are now parametrised by converting the parameter dataclass instances to kwargs, using some injectable conversions along the way
344 lines
12 KiB
Python
344 lines
12 KiB
Python
from abc import abstractmethod
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from collections.abc import Sequence
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from dataclasses import dataclass
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from pprint import pprint
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from typing import Generic, TypeVar, Callable
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import numpy as np
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import torch
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from tianshou.data import Collector
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from tianshou.highlevel.agent import AgentFactory, PPOAgentFactory, SACAgentFactory
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from tianshou.highlevel.config import RLSamplingConfig
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from tianshou.highlevel.env import EnvFactory
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from tianshou.highlevel.logger import DefaultLoggerFactory, LoggerFactory
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from tianshou.highlevel.module import (
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ActorFactory,
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CriticFactory,
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DefaultActorFactory,
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DefaultCriticFactory,
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)
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from tianshou.highlevel.optim import AdamOptimizerFactory, OptimizerFactory
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from tianshou.highlevel.params.policy_params import PPOParams, SACParams
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from tianshou.policy import BasePolicy
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from tianshou.policy.modelfree.pg import TDistParams
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from tianshou.trainer import BaseTrainer
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TPolicy = TypeVar("TPolicy", bound=BasePolicy)
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TTrainer = TypeVar("TTrainer", bound=BaseTrainer)
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@dataclass
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class RLExperimentConfig:
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"""Generic config for setting up the experiment, not RL or training specific."""
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seed: int = 42
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render: float | None = 0.0
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"""Milliseconds between rendered frames; if None, no rendering"""
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device: str = "cuda" if torch.cuda.is_available() else "cpu"
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resume_id: str | None = None
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"""For restoring a model and running means of env-specifics from a checkpoint"""
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resume_path: str | None = None
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"""For restoring a model and running means of env-specifics from a checkpoint"""
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watch: bool = False
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"""If True, will not perform training and only watch the restored policy"""
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watch_num_episodes = 10
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class RLExperiment(Generic[TPolicy, TTrainer]):
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def __init__(
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self,
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config: RLExperimentConfig,
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env_factory: EnvFactory,
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agent_factory: AgentFactory,
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logger_factory: LoggerFactory | None = None,
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):
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if logger_factory is None:
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logger_factory = DefaultLoggerFactory()
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self.config = config
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self.env_factory = env_factory
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self.agent_factory = agent_factory
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self.logger_factory = logger_factory
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def _set_seed(self) -> None:
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seed = self.config.seed
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np.random.seed(seed)
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torch.manual_seed(seed)
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def _build_config_dict(self) -> dict:
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return {
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# TODO
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}
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def run(self, log_name: str) -> None:
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self._set_seed()
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envs = self.env_factory.create_envs()
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full_config = self._build_config_dict()
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full_config.update(envs.info())
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run_id = self.config.resume_id
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logger = self.logger_factory.create_logger(
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log_name=log_name,
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run_id=run_id,
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config_dict=full_config,
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)
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policy = self.agent_factory.create_policy(envs, self.config.device)
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if self.config.resume_path:
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self.agent_factory.load_checkpoint(
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policy,
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self.config.resume_path,
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envs,
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self.config.device,
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)
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train_collector, test_collector = self.agent_factory.create_train_test_collector(
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policy,
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envs,
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)
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if not self.config.watch:
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trainer = self.agent_factory.create_trainer(
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policy,
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train_collector,
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test_collector,
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envs,
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logger,
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)
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result = trainer.run()
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pprint(result) # TODO logging
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self._watch_agent(
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self.config.watch_num_episodes,
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policy,
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test_collector,
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self.config.render,
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)
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@staticmethod
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def _watch_agent(num_episodes, policy: BasePolicy, test_collector: Collector, render) -> None:
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policy.eval()
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test_collector.reset()
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result = test_collector.collect(n_episode=num_episodes, render=render)
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print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
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TBuilder = TypeVar("TBuilder", bound="RLExperimentBuilder")
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class RLExperimentBuilder:
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def __init__(
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self,
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experiment_config: RLExperimentConfig,
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env_factory: EnvFactory,
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sampling_config: RLSamplingConfig,
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):
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self._config = experiment_config
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self._env_factory = env_factory
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self._sampling_config = sampling_config
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self._logger_factory: LoggerFactory | None = None
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self._optim_factory: OptimizerFactory | None = None
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def with_logger_factory(self: TBuilder, logger_factory: LoggerFactory) -> TBuilder:
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self._logger_factory = logger_factory
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return self
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def with_optim_factory(self: TBuilder, optim_factory: OptimizerFactory) -> TBuilder:
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self._optim_factory = optim_factory
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return self
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def with_optim_factory_default(
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self: TBuilder, betas=(0.9, 0.999), eps=1e-08, weight_decay=0,
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) -> TBuilder:
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"""Configures the use of the default optimizer, Adam, with the given parameters.
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:param betas: coefficients used for computing running averages of gradient and its square
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:param eps: term added to the denominator to improve numerical stability
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:param weight_decay: weight decay (L2 penalty)
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:return: the builder
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"""
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self._optim_factory = AdamOptimizerFactory(betas=betas, eps=eps, weight_decay=weight_decay)
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return self
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@abstractmethod
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def _create_agent_factory(self) -> AgentFactory:
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pass
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def _get_optim_factory(self) -> OptimizerFactory:
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if self._optim_factory is None:
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return AdamOptimizerFactory()
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else:
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return self._optim_factory
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def build(self) -> RLExperiment:
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return RLExperiment(
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self._config, self._env_factory, self._create_agent_factory(), self._logger_factory,
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)
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class _BuilderMixinActorFactory:
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def __init__(self):
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self._actor_factory: ActorFactory | None = None
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def with_actor_factory(self: TBuilder, actor_factory: ActorFactory) -> TBuilder:
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self: TBuilder | _BuilderMixinActorFactory
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self._actor_factory = actor_factory
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return self
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def with_actor_factory_default(
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self: TBuilder,
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hidden_sizes: Sequence[int],
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continuous_unbounded=False,
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continuous_conditioned_sigma=False,
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) -> TBuilder:
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self: TBuilder | _BuilderMixinActorFactory
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self._actor_factory = DefaultActorFactory(
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hidden_sizes,
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continuous_unbounded=continuous_unbounded,
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continuous_conditioned_sigma=continuous_conditioned_sigma,
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)
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return self
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def _get_actor_factory(self):
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if self._actor_factory is None:
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return DefaultActorFactory()
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else:
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return self._actor_factory
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class _BuilderMixinCriticsFactory:
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def __init__(self, num_critics: int):
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self._critic_factories: list[CriticFactory | None] = [None] * num_critics
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def _with_critic_factory(self, idx: int, critic_factory: CriticFactory):
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self._critic_factories[idx] = critic_factory
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return self
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def _with_critic_factory_default(self, idx: int, hidden_sizes: Sequence[int]):
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self._critic_factories[idx] = DefaultCriticFactory(hidden_sizes)
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return self
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def _get_critic_factory(self, idx: int):
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factory = self._critic_factories[idx]
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if factory is None:
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return DefaultCriticFactory()
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else:
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return factory
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class _BuilderMixinSingleCriticFactory(_BuilderMixinCriticsFactory):
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def __init__(self):
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super().__init__(1)
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def with_critic_factory(self: TBuilder, critic_factory: CriticFactory) -> TBuilder:
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self: TBuilder | "_BuilderMixinSingleCriticFactory"
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self._with_critic_factory(0, critic_factory)
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return self
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def with_critic_factory_default(
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self: TBuilder, hidden_sizes: Sequence[int] = DefaultCriticFactory.DEFAULT_HIDDEN_SIZES,
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) -> TBuilder:
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self: TBuilder | "_BuilderMixinSingleCriticFactory"
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self._with_critic_factory_default(0, hidden_sizes)
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return self
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class _BuilderMixinDualCriticFactory(_BuilderMixinCriticsFactory):
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def __init__(self):
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super().__init__(2)
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def with_common_critic_factory(self: TBuilder, critic_factory: CriticFactory) -> TBuilder:
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self: TBuilder | "_BuilderMixinDualCriticFactory"
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for i in range(len(self._critic_factories)):
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self._with_critic_factory(i, critic_factory)
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return self
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def with_common_critic_factory_default(
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self, hidden_sizes: Sequence[int] = DefaultCriticFactory.DEFAULT_HIDDEN_SIZES,
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) -> TBuilder:
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self: TBuilder | "_BuilderMixinDualCriticFactory"
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for i in range(len(self._critic_factories)):
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self._with_critic_factory_default(i, hidden_sizes)
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return self
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def with_critic1_factory(self: TBuilder, critic_factory: CriticFactory) -> TBuilder:
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self: TBuilder | "_BuilderMixinDualCriticFactory"
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self._with_critic_factory(0, critic_factory)
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return self
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def with_critic1_factory_default(
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self, hidden_sizes: Sequence[int] = DefaultCriticFactory.DEFAULT_HIDDEN_SIZES,
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) -> TBuilder:
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self: TBuilder | "_BuilderMixinDualCriticFactory"
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self._with_critic_factory_default(0, hidden_sizes)
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return self
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def with_critic2_factory(self: TBuilder, critic_factory: CriticFactory) -> TBuilder:
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self: TBuilder | "_BuilderMixinDualCriticFactory"
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self._with_critic_factory(1, critic_factory)
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return self
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def with_critic2_factory_default(
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self, hidden_sizes: Sequence[int] = DefaultCriticFactory.DEFAULT_HIDDEN_SIZES,
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) -> TBuilder:
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self: TBuilder | "_BuilderMixinDualCriticFactory"
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self._with_critic_factory_default(0, hidden_sizes)
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return self
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class PPOExperimentBuilder(
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RLExperimentBuilder, _BuilderMixinActorFactory, _BuilderMixinSingleCriticFactory,
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):
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def __init__(
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self,
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experiment_config: RLExperimentConfig,
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env_factory: EnvFactory,
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sampling_config: RLSamplingConfig,
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dist_fn: Callable[[TDistParams], torch.distributions.Distribution],
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):
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super().__init__(experiment_config, env_factory, sampling_config)
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_BuilderMixinActorFactory.__init__(self)
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_BuilderMixinSingleCriticFactory.__init__(self)
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self._params: PPOParams = PPOParams()
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self._dist_fn = dist_fn
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def with_ppo_params(self, params: PPOParams) -> "PPOExperimentBuilder":
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self._params = params
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return self
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@abstractmethod
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def _create_agent_factory(self) -> AgentFactory:
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return PPOAgentFactory(
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self._params,
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self._sampling_config,
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self._get_actor_factory(),
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self._get_critic_factory(0),
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self._get_optim_factory(),
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self._dist_fn
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)
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class SACExperimentBuilder(
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RLExperimentBuilder, _BuilderMixinActorFactory, _BuilderMixinDualCriticFactory,
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):
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def __init__(
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self,
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experiment_config: RLExperimentConfig,
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env_factory: EnvFactory,
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sampling_config: RLSamplingConfig,
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):
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super().__init__(experiment_config, env_factory, sampling_config)
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_BuilderMixinActorFactory.__init__(self)
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_BuilderMixinDualCriticFactory.__init__(self)
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self._params: SACParams = SACParams()
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def with_sac_params(self, params: SACParams) -> "SACExperimentBuilder":
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self._params = params
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return self
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def _create_agent_factory(self) -> AgentFactory:
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return SACAgentFactory(self._params, self._sampling_config, self._get_actor_factory(),
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self._get_critic_factory(0), self._get_critic_factory(1), self._get_optim_factory())
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