TRPL is da
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pass
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import warnings
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from typing import Any, ClassVar, Dict, Optional, Type, TypeVar, Union
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import numpy as np
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import torch as th
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from gymnasium import spaces
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from torch.nn import functional as F
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from stable_baselines3.common.buffers import RolloutBuffer
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from ..common.on_policy_algorithm import BetterOnPolicyAlgorithm
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from ..common.policies import ActorCriticPolicy, BasePolicy
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from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
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from stable_baselines3.common.utils import explained_variance, get_schedule_fn
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#from metastable_baselines2 import PPO
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SelfTRPL = TypeVar("SelfTRPL", bound="TRPL")
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class TRPL(BetterOnPolicyAlgorithm):
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"""
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TODO: Bla
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:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: The learning rate, it can be a function
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of the current progress remaining (from 1 to 0)
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:param n_steps: The number of steps to run for each environment per update
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(i.e. rollout buffer size is n_steps * n_envs where n_envs is number of environment copies running in parallel)
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NOTE: n_steps * n_envs must be greater than 1 (because of the advantage normalization)
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See https://github.com/pytorch/pytorch/issues/29372
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:param batch_size: Minibatch size
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:param n_epochs: Number of epoch when optimizing the surrogate loss
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:param gamma: Discount factor
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:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
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:param clip_range: Clipping parameter, it can be a function of the current progress
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remaining (from 1 to 0).
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:param clip_range_vf: Clipping parameter for the value function,
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it can be a function of the current progress remaining (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 normalize_advantage: Whether to normalize or not the advantage
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:param ent_coef: Entropy coefficient for the loss calculation
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:param vf_coef: Value function coefficient for the loss calculation
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:param max_grad_norm: The maximum value for the gradient clipping
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:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
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Default: -1 (only sample at the beginning of the rollout)
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:param use_pca: Wether to use Prior Conditioned Annealing
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:param rollout_buffer_class: Rollout buffer class to use. If ``None``, it will be automatically selected.
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:param rollout_buffer_kwargs: Keyword arguments to pass to the rollout buffer on creation
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:param target_kl: 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 stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
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the reported success rate, mean episode length, and mean reward over
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:param tensorboard_log: the log location for tensorboard (if None, no logging)
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:param policy_kwargs: additional arguments to be passed to the policy on creation
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:param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
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debug messages
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:param seed: Seed for the pseudo random generators
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:param 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: Whether or not to build the network at the creation of the instance
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"""
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policy_aliases: ClassVar[Dict[str, Type[BasePolicy]]] = {
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"MlpPolicy": ActorCriticPolicy
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}
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def __init__(
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self,
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policy: Union[str, Type[ActorCriticPolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Schedule] = 3e-4,
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n_steps: int = 2048,
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batch_size: 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: Union[float, Schedule] = 0.2,
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clip_range_vf: Union[None, float, Schedule] = None,
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normalize_advantage: bool = True,
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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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use_pca: bool = False,
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rollout_buffer_class: Optional[Type[RolloutBuffer]] = None,
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rollout_buffer_kwargs: Optional[Dict[str, Any]] = None,
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target_kl: Optional[float] = None,
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stats_window_size: int = 100,
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tensorboard_log: Optional[str] = None,
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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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):
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super().__init__(
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policy,
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env,
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learning_rate=learning_rate,
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n_steps=n_steps,
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gamma=gamma,
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gae_lambda=gae_lambda,
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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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use_sde=use_sde,
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sde_sample_freq=sde_sample_freq,
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use_pca=use_pca,
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rollout_buffer_class=rollout_buffer_class,
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rollout_buffer_kwargs=rollout_buffer_kwargs,
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stats_window_size=stats_window_size,
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tensorboard_log=tensorboard_log,
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policy_kwargs=policy_kwargs,
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verbose=verbose,
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device=device,
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seed=seed,
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_init_setup_model=False,
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supported_action_spaces=(
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spaces.Box,
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spaces.Discrete,
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spaces.MultiDiscrete,
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spaces.MultiBinary,
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),
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)
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print('[i] Using metastable version of TRPL')
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# Sanity check, otherwise it will lead to noisy gradient and NaN
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# because of the advantage normalization
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if normalize_advantage:
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assert (
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batch_size > 1
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), "`batch_size` must be greater than 1. See https://github.com/DLR-RM/stable-baselines3/issues/440"
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if self.env is not None:
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# Check that `n_steps * n_envs > 1` to avoid NaN
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# when doing advantage normalization
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buffer_size = self.env.num_envs * self.n_steps
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assert buffer_size > 1 or (
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not normalize_advantage
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), f"`n_steps * n_envs` must be greater than 1. Currently n_steps={self.n_steps} and n_envs={self.env.num_envs}"
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# Check that the rollout buffer size is a multiple of the mini-batch size
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untruncated_batches = buffer_size // batch_size
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if buffer_size % batch_size > 0:
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warnings.warn(
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f"You have specified a mini-batch size of {batch_size},"
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f" but because the `RolloutBuffer` is of size `n_steps * n_envs = {buffer_size}`,"
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f" after every {untruncated_batches} untruncated mini-batches,"
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f" there will be a truncated mini-batch of size {buffer_size % batch_size}\n"
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f"We recommend using a `batch_size` that is a factor of `n_steps * n_envs`.\n"
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f"Info: (n_steps={self.n_steps} and n_envs={self.env.num_envs})"
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)
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self.batch_size = batch_size
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self.n_epochs = n_epochs
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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.normalize_advantage = normalize_advantage
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self.target_kl = target_kl
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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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super()._setup_model()
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# Initialize schedules for policy/value clipping
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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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if isinstance(self.clip_range_vf, (float, int)):
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assert self.clip_range_vf > 0, "`clip_range_vf` must be positive, " "pass `None` to deactivate vf clipping"
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self.clip_range_vf = get_schedule_fn(self.clip_range_vf)
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def train(self) -> None:
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"""
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Update policy using the currently gathered rollout buffer.
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"""
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# Switch to train mode (this affects batch norm / dropout)
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self.policy.set_training_mode(True)
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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_remaining)
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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_remaining)
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entropy_losses = []
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pg_losses, value_losses = [], []
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clip_fractions = []
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continue_training = True
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# train for n_epochs epochs
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for epoch in range(self.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(self.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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if self.use_sde or self.use_pca:
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self.policy.reset_noise(self.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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# Normalization does not make sense if mini batchsize == 1, see GH issue #325
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if self.normalize_advantage and len(advantages) > 1:
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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 difference 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(
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values - rollout_data.old_values, -clip_range_vf, clip_range_vf
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)
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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 = -th.mean(-log_prob)
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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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# Calculate approximate form of reverse KL Divergence for early stopping
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# see issue #417: https://github.com/DLR-RM/stable-baselines3/issues/417
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# and discussion in PR #419: https://github.com/DLR-RM/stable-baselines3/pull/419
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# and Schulman blog: http://joschu.net/blog/kl-approx.html
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with th.no_grad():
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log_ratio = log_prob - rollout_data.old_log_prob
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approx_kl_div = th.mean((th.exp(log_ratio) - 1) - log_ratio).cpu().numpy()
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approx_kl_divs.append(approx_kl_div)
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if self.target_kl is not None and approx_kl_div > 1.5 * self.target_kl:
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continue_training = False
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if self.verbose >= 1:
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print(f"Early stopping at step {epoch} due to reaching max kl: {approx_kl_div:.2f}")
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break
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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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self._n_updates += 1
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if not continue_training:
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break
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explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten())
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# Logs
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self.logger.record("train/entropy_loss", np.mean(entropy_losses))
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self.logger.record("train/policy_gradient_loss", np.mean(pg_losses))
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self.logger.record("train/value_loss", np.mean(value_losses))
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self.logger.record("train/approx_kl", np.mean(approx_kl_divs))
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self.logger.record("train/clip_fraction", np.mean(clip_fractions))
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self.logger.record("train/loss", loss.item())
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self.logger.record("train/explained_variance", explained_var)
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if hasattr(self.policy, "log_std"):
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self.logger.record("train/std", th.exp(self.policy.log_std).mean().item())
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self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
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self.logger.record("train/clip_range", clip_range)
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if self.clip_range_vf is not None:
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self.logger.record("train/clip_range_vf", clip_range_vf)
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def learn(
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self: SelfPPO,
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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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tb_log_name: str = "PPO",
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reset_num_timesteps: bool = True,
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progress_bar: bool = False,
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) -> SelfPPO:
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return super().learn(
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total_timesteps=total_timesteps,
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callback=callback,
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log_interval=log_interval,
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tb_log_name=tb_log_name,
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reset_num_timesteps=reset_num_timesteps,
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progress_bar=progress_bar,
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)
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