mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-28 22:56:53 +00:00
365 lines
17 KiB
Python
365 lines
17 KiB
Python
import os
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import time
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from typing import List, Tuple, Type, Union, Callable, Optional, Dict, Any
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import gym
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from gym import spaces
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import torch as th
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import torch.nn.functional as F
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# Check if tensorboard is available for pytorch
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try:
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from torch.utils.tensorboard import SummaryWriter
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except ImportError:
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SummaryWriter = None
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import numpy as np
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from torchy_baselines.common import logger
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from torchy_baselines.common.base_class import BaseRLModel
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from torchy_baselines.common.type_aliases import GymEnv, MaybeCallback
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from torchy_baselines.common.buffers import RolloutBuffer
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from torchy_baselines.common.utils import explained_variance, get_schedule_fn
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from torchy_baselines.common.vec_env import VecEnv
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from torchy_baselines.common.callbacks import BaseCallback
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from torchy_baselines.ppo.policies import PPOPolicy
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class PPO(BaseRLModel):
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"""
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Proximal Policy Optimization algorithm (PPO) (clip version)
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Paper: https://arxiv.org/abs/1707.06347
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Code: This implementation borrows code from OpenAI Spinning Up (https://github.com/openai/spinningup/)
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https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and
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and Stable Baselines (PPO2 from https://github.com/hill-a/stable-baselines)
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Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html
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:param policy: (PPOPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: (float or callable) The learning rate, it can be a function
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of the current progress (from 1 to 0)
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:param n_steps: (int) The number of steps to run for each environment per update
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(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
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:param batch_size: (int) Minibatch size
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:param n_epochs: (int) Number of epoch when optimizing the surrogate loss
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:param gamma: (float) Discount factor
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:param gae_lambda: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator
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:param clip_range: (float or callable) Clipping parameter, it can be a function of the current progress
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(from 1 to 0).
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:param clip_range_vf: (float or callable) Clipping parameter for the value function,
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it can be a function of the current progress (from 1 to 0).
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This is a parameter specific to the OpenAI implementation. If None is passed (default),
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no clipping will be done on the value function.
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IMPORTANT: this clipping depends on the reward scaling.
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:param ent_coef: (float) Entropy coefficient for the loss calculation
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:param vf_coef: (float) Value function coefficient for the loss calculation
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:param max_grad_norm: (float) The maximum value for the gradient clipping
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:param use_sde: (bool) Whether to use State Dependent Exploration (SDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE
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Default: -1 (only sample at the beginning of the rollout)
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:param target_kl: (float) Limit the KL divergence between updates,
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because the clipping is not enough to prevent large update
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see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213)
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By default, there is no limit on the kl div.
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:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
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:param create_eval_env: (bool) Whether to create a second environment that will be
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used for evaluating the agent periodically. (Only available when passing string for the environment)
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:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
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:param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug
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:param seed: (int) Seed for the pseudo random generators
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:param device: (str or th.device) Device (cpu, cuda, ...) on which the code should be run.
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Setting it to auto, the code will be run on the GPU if possible.
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:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
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"""
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def __init__(self, policy: Union[str, Type[PPOPolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Callable] = 3e-4,
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n_steps: int = 2048,
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batch_size: Optional[int] = 64,
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n_epochs: int = 10,
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gamma: float = 0.99,
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gae_lambda: float = 0.95,
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clip_range: float = 0.2,
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clip_range_vf: Optional[float] = None,
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ent_coef: float = 0.0,
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vf_coef: float = 0.5,
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max_grad_norm: float = 0.5,
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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target_kl: Optional[float] = None,
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tensorboard_log: Optional[str] = None,
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create_eval_env: bool = False,
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policy_kwargs: Optional[Dict[str, Any]] = None,
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verbose: int = 0,
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seed: Optional[int] = None,
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device: Union[th.device, str] = 'auto',
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_init_setup_model: bool = True):
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super(PPO, self).__init__(policy, env, PPOPolicy, policy_kwargs=policy_kwargs,
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verbose=verbose, device=device, use_sde=use_sde, sde_sample_freq=sde_sample_freq,
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create_eval_env=create_eval_env, support_multi_env=True, seed=seed)
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self.learning_rate = learning_rate
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self.batch_size = batch_size
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self.n_epochs = n_epochs
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self.n_steps = n_steps
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self.gamma = gamma
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self.gae_lambda = gae_lambda
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self.clip_range = clip_range
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self.clip_range_vf = clip_range_vf
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self.ent_coef = ent_coef
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self.vf_coef = vf_coef
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self.max_grad_norm = max_grad_norm
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self.rollout_buffer = None
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self.target_kl = target_kl
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self.tensorboard_log = tensorboard_log
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self.tb_writer = None
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if _init_setup_model:
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self._setup_model()
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def _setup_model(self) -> None:
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self._setup_lr_schedule()
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# TODO: preprocessing: one hot vector for obs discrete
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state_dim = self.observation_space.shape[0]
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if isinstance(self.action_space, spaces.Box):
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# Action is a 1D vector
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action_dim = self.action_space.shape[0]
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elif isinstance(self.action_space, spaces.Discrete):
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# Action is a scalar
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action_dim = 1
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self.set_random_seed(self.seed)
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self.rollout_buffer = RolloutBuffer(self.n_steps, state_dim, action_dim, self.device,
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gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.n_envs)
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self.policy = self.policy_class(self.observation_space, self.action_space,
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self.lr_schedule, use_sde=self.use_sde, device=self.device,
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**self.policy_kwargs)
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self.policy = self.policy.to(self.device)
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self.clip_range = get_schedule_fn(self.clip_range)
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if self.clip_range_vf is not None:
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self.clip_range_vf = get_schedule_fn(self.clip_range_vf)
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def collect_rollouts(self,
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env: VecEnv,
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callback: BaseCallback,
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rollout_buffer: RolloutBuffer,
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n_rollout_steps: int = 256,
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obs: Optional[np.ndarray] = None) -> Tuple[Optional[np.ndarray], bool]:
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n_steps = 0
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continue_training = True
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rollout_buffer.reset()
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# Sample new weights for the state dependent exploration
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if self.use_sde:
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self.policy.reset_noise(env.num_envs)
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callback.on_rollout_start()
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while n_steps < n_rollout_steps:
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if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0:
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# Sample a new noise matrix
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self.policy.reset_noise(env.num_envs)
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with th.no_grad():
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actions, values, log_probs = self.policy.forward(obs)
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actions = actions.cpu().numpy()
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# Rescale and perform action
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clipped_actions = actions
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# Clip the actions to avoid out of bound error
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if isinstance(self.action_space, gym.spaces.Box):
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clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high)
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new_obs, rewards, dones, infos = env.step(clipped_actions)
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if callback.on_step() is False:
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continue_training = False
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return None, continue_training
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self._update_info_buffer(infos)
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n_steps += 1
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self.num_timesteps += env.num_envs
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if isinstance(self.action_space, gym.spaces.Discrete):
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# Reshape in case of discrete action
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actions = actions.reshape(-1, 1)
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rollout_buffer.add(obs, actions, rewards, dones, values, log_probs)
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obs = new_obs
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rollout_buffer.compute_returns_and_advantage(values, dones=dones)
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callback.on_rollout_end()
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return obs, continue_training
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def train(self, n_epochs: int, batch_size: int = 64) -> None:
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# Update optimizer learning rate
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self._update_learning_rate(self.policy.optimizer)
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# Compute current clip range
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clip_range = self.clip_range(self._current_progress)
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# Optional: clip range for the value function
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if self.clip_range_vf is not None:
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clip_range_vf = self.clip_range_vf(self._current_progress)
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entropy_losses, all_kl_divs = [], []
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pg_losses, value_losses = [], []
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clip_fractions = []
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# train for gradient_steps epochs
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for epoch in range(n_epochs):
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approx_kl_divs = []
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# Do a complete pass on the rollout buffer
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for rollout_data in self.rollout_buffer.get(batch_size):
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actions = rollout_data.actions
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if isinstance(self.action_space, spaces.Discrete):
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# Convert discrete action from float to long
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actions = rollout_data.actions.long().flatten()
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# Re-sample the noise matrix because the log_std has changed
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# TODO: investigate why there is no issue with the gradient
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# if that line is commented (as in SAC)
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if self.use_sde:
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self.policy.reset_noise(batch_size)
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values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions)
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values = values.flatten()
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# Normalize advantage
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advantages = rollout_data.advantages
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advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
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# ratio between old and new policy, should be one at the first iteration
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ratio = th.exp(log_prob - rollout_data.old_log_prob)
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# clipped surrogate loss
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policy_loss_1 = advantages * ratio
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policy_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range)
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policy_loss = -th.min(policy_loss_1, policy_loss_2).mean()
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# Logging
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pg_losses.append(policy_loss.item())
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clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()).item()
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clip_fractions.append(clip_fraction)
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if self.clip_range_vf is None:
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# No clipping
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values_pred = values
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else:
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# Clip the different between old and new value
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# NOTE: this depends on the reward scaling
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values_pred = rollout_data.old_values + th.clamp(values - rollout_data.old_values, -clip_range_vf,
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clip_range_vf)
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# Value loss using the TD(gae_lambda) target
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value_loss = F.mse_loss(rollout_data.returns, values_pred)
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value_losses.append(value_loss.item())
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# Entropy loss favor exploration
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if entropy is None:
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# Approximate entropy when no analytical form
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entropy_loss = -log_prob.mean()
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else:
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entropy_loss = -th.mean(entropy)
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entropy_losses.append(entropy_loss.item())
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loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
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# Optimization step
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self.policy.optimizer.zero_grad()
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loss.backward()
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# Clip grad norm
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th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
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self.policy.optimizer.step()
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approx_kl_divs.append(th.mean(rollout_data.old_log_prob - log_prob).detach().cpu().numpy())
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all_kl_divs.append(np.mean(approx_kl_divs))
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if self.target_kl is not None and np.mean(approx_kl_divs) > 1.5 * self.target_kl:
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print(f"Early stopping at step {epoch} due to reaching max kl: {np.mean(approx_kl_divs):.2f}")
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break
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self._n_updates += n_epochs
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explained_var = explained_variance(self.rollout_buffer.returns.flatten(),
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self.rollout_buffer.values.flatten())
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logger.logkv("n_updates", self._n_updates)
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logger.logkv("clip_fraction", np.mean(clip_fraction))
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logger.logkv("clip_range", clip_range)
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if self.clip_range_vf is not None:
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logger.logkv("clip_range_vf", clip_range_vf)
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logger.logkv("approx_kl", np.mean(approx_kl_divs))
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logger.logkv("explained_variance", explained_var)
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logger.logkv("entropy_loss", np.mean(entropy_losses))
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logger.logkv("policy_gradient_loss", np.mean(pg_losses))
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logger.logkv("value_loss", np.mean(value_losses))
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if hasattr(self.policy, 'log_std'):
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logger.logkv("std", th.exp(self.policy.log_std).mean().item())
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def learn(self,
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total_timesteps: int,
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callback: MaybeCallback = None,
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log_interval: int = 1,
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eval_env: Optional[GymEnv] = None,
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eval_freq: int = -1,
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n_eval_episodes: int = 5,
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tb_log_name: str = "PPO",
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eval_log_path: Optional[str] = None,
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reset_num_timesteps: bool = True) -> 'PPO':
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episode_num, obs, callback = self._setup_learn(eval_env, callback, eval_freq,
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n_eval_episodes, eval_log_path, reset_num_timesteps)
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iteration = 0
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if self.tensorboard_log is not None and SummaryWriter is not None:
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self.tb_writer = SummaryWriter(log_dir=os.path.join(self.tensorboard_log, tb_log_name))
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callback.on_training_start(locals(), globals())
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while self.num_timesteps < total_timesteps:
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obs, continue_training = self.collect_rollouts(self.env, callback,
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self.rollout_buffer,
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n_rollout_steps=self.n_steps,
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obs=obs)
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if continue_training is False:
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break
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iteration += 1
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self._update_current_progress(self.num_timesteps, total_timesteps)
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# Display training infos
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if self.verbose >= 1 and log_interval is not None and iteration % log_interval == 0:
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fps = int(self.num_timesteps / (time.time() - self.start_time))
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logger.logkv("iterations", iteration)
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if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
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logger.logkv('ep_rew_mean', self.safe_mean([ep_info['r'] for ep_info in self.ep_info_buffer]))
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logger.logkv('ep_len_mean', self.safe_mean([ep_info['l'] for ep_info in self.ep_info_buffer]))
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logger.logkv("fps", fps)
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logger.logkv('time_elapsed', int(time.time() - self.start_time))
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logger.logkv("total timesteps", self.num_timesteps)
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logger.dumpkvs()
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self.train(self.n_epochs, batch_size=self.batch_size)
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# For tensorboard integration
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# if self.tb_writer is not None:
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# self.tb_writer.add_scalar('Eval/reward', mean_reward, self.num_timesteps)
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callback.on_training_end()
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return self
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def get_torch_variables(self) -> Tuple[List[str], List[str]]:
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"""
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cf base class
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"""
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state_dicts = ["policy", "policy.optimizer"]
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return state_dicts, []
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