testbed/DLR-RM__stable-baselines3/stable_baselines3/common/utils.py

import glob
import os
import random
from collections import deque
from itertools import zip_longest
from typing import Iterable, Optional, Union

import gym
import numpy as np
import torch as th

# Check if tensorboard is available for pytorch
try:
    from torch.utils.tensorboard import SummaryWriter
except ImportError:
    SummaryWriter = None

from stable_baselines3.common import logger
from stable_baselines3.common.type_aliases import GymEnv, Schedule


def set_random_seed(seed: int, using_cuda: bool = False) -> None:
    """
    Seed the different random generators
    :param seed:
    :param using_cuda:
    """
    # Seed python RNG
    random.seed(seed)
    # Seed numpy RNG
    np.random.seed(seed)
    # seed the RNG for all devices (both CPU and CUDA)
    th.manual_seed(seed)

    if using_cuda:
        # Deterministic operations for CuDNN, it may impact performances
        th.backends.cudnn.deterministic = True
        th.backends.cudnn.benchmark = False


# From stable baselines
def explained_variance(y_pred: np.ndarray, y_true: np.ndarray) -> np.ndarray:
    """
    Computes fraction of variance that ypred explains about y.
    Returns 1 - Var[y-ypred] / Var[y]

    interpretation:
        ev=0  =>  might as well have predicted zero
        ev=1  =>  perfect prediction
        ev<0  =>  worse than just predicting zero

    :param y_pred: the prediction
    :param y_true: the expected value
    :return: explained variance of ypred and y
    """
    assert y_true.ndim == 1 and y_pred.ndim == 1
    var_y = np.var(y_true)
    return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y


def update_learning_rate(optimizer: th.optim.Optimizer, learning_rate: float) -> None:
    """
    Update the learning rate for a given optimizer.
    Useful when doing linear schedule.

    :param optimizer:
    :param learning_rate:
    """
    for param_group in optimizer.param_groups:
        param_group["lr"] = learning_rate


def get_schedule_fn(value_schedule: Union[Schedule, float, int]) -> Schedule:
    """
    Transform (if needed) learning rate and clip range (for PPO)
    to callable.

    :param value_schedule:
    :return:
    """
    # If the passed schedule is a float
    # create a constant function
    if isinstance(value_schedule, (float, int)):
        # Cast to float to avoid errors
        value_schedule = constant_fn(float(value_schedule))
    else:
        assert callable(value_schedule)
    return value_schedule


def get_linear_fn(start: float, end: float, end_fraction: float) -> Schedule:
    """
    Create a function that interpolates linearly between start and end
    between ``progress_remaining`` = 1 and ``progress_remaining`` = ``end_fraction``.
    This is used in DQN for linearly annealing the exploration fraction
    (epsilon for the epsilon-greedy strategy).

    :params start: value to start with if ``progress_remaining`` = 1
    :params end: value to end with if ``progress_remaining`` = 0
    :params end_fraction: fraction of ``progress_remaining``
        where end is reached e.g 0.1 then end is reached after 10%
        of the complete training process.
    :return:
    """

    def func(progress_remaining: float) -> float:
        if (1 - progress_remaining) > end_fraction:
            return end
        else:
            return start + (1 - progress_remaining) * (end - start) / end_fraction

    return func


def constant_fn(val: float) -> Schedule:
    """
    Create a function that returns a constant
    It is useful for learning rate schedule (to avoid code duplication)

    :param val:
    :return:
    """

    def func(_):
        return val

    return func


def get_device(device: Union[th.device, str] = "auto") -> th.device:
    """
    Retrieve PyTorch device.
    It checks that the requested device is available first.
    For now, it supports only cpu and cuda.
    By default, it tries to use the gpu.

    :param device: One for 'auto', 'cuda', 'cpu'
    :return:
    """
    # Cuda by default
    if device == "auto":
        device = "cuda"
    # Force conversion to th.device
    device = th.device(device)

    # Cuda not available
    if device.type == th.device("cuda").type and not th.cuda.is_available():
        return th.device("cpu")

    return device


def get_latest_run_id(log_path: Optional[str] = None, log_name: str = "") -> int:
    """
    Returns the latest run number for the given log name and log path,
    by finding the greatest number in the directories.

    :return: latest run number
    """
    max_run_id = 0
    for path in glob.glob(f"{log_path}/{log_name}_[0-9]*"):
        file_name = path.split(os.sep)[-1]
        ext = file_name.split("_")[-1]
        if log_name == "_".join(file_name.split("_")[:-1]) and ext.isdigit() and int(ext) > max_run_id:
            max_run_id = int(ext)
    return max_run_id


def configure_logger(
    verbose: int = 0, tensorboard_log: Optional[str] = None, tb_log_name: str = "", reset_num_timesteps: bool = True
) -> None:
    """
    Configure the logger's outputs.

    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
    :param tensorboard_log: the log location for tensorboard (if None, no logging)
    :param tb_log_name: tensorboard log
    """
    if tensorboard_log is not None and SummaryWriter is not None:
        latest_run_id = get_latest_run_id(tensorboard_log, tb_log_name)
        if not reset_num_timesteps:
            # Continue training in the same directory
            latest_run_id -= 1
        save_path = os.path.join(tensorboard_log, f"{tb_log_name}_{latest_run_id + 1}")
        if verbose >= 1:
            logger.configure(save_path, ["stdout", "tensorboard"])
        else:
            logger.configure(save_path, ["tensorboard"])
    elif verbose == 0:
        logger.configure(format_strings=[""])


def check_for_correct_spaces(env: GymEnv, observation_space: gym.spaces.Space, action_space: gym.spaces.Space) -> None:
    """
    Checks that the environment has same spaces as provided ones. Used by BaseAlgorithm to check if
    spaces match after loading the model with given env.
    Checked parameters:
    - observation_space
    - action_space

    :param env: Environment to check for valid spaces
    :param observation_space: Observation space to check against
    :param action_space: Action space to check against
    """
    if observation_space != env.observation_space:
        raise ValueError(f"Observation spaces do not match: {observation_space} != {env.observation_space}")
    if action_space != env.action_space:
        raise ValueError(f"Action spaces do not match: {action_space} != {env.action_space}")


def is_vectorized_observation(observation: np.ndarray, observation_space: gym.spaces.Space) -> bool:
    """
    For every observation type, detects and validates the shape,
    then returns whether or not the observation is vectorized.

    :param observation: the input observation to validate
    :param observation_space: the observation space
    :return: whether the given observation is vectorized or not
    """
    if isinstance(observation_space, gym.spaces.Box):
        if observation.shape == observation_space.shape:
            return False
        elif observation.shape[1:] == observation_space.shape:
            return True
        else:
            raise ValueError(
                f"Error: Unexpected observation shape {observation.shape} for "
                + f"Box environment, please use {observation_space.shape} "
                + "or (n_env, {}) for the observation shape.".format(", ".join(map(str, observation_space.shape)))
            )
    elif isinstance(observation_space, gym.spaces.Discrete):
        if observation.shape == ():  # A numpy array of a number, has shape empty tuple '()'
            return False
        elif len(observation.shape) == 1:
            return True
        else:
            raise ValueError(
                f"Error: Unexpected observation shape {observation.shape} for "
                + "Discrete environment, please use (1,) or (n_env, 1) for the observation shape."
            )

    elif isinstance(observation_space, gym.spaces.MultiDiscrete):
        if observation.shape == (len(observation_space.nvec),):
            return False
        elif len(observation.shape) == 2 and observation.shape[1] == len(observation_space.nvec):
            return True
        else:
            raise ValueError(
                f"Error: Unexpected observation shape {observation.shape} for MultiDiscrete "
                + f"environment, please use ({len(observation_space.nvec)},) or "
                + f"(n_env, {len(observation_space.nvec)}) for the observation shape."
            )
    elif isinstance(observation_space, gym.spaces.MultiBinary):
        if observation.shape == (observation_space.n,):
            return False
        elif len(observation.shape) == 2 and observation.shape[1] == observation_space.n:
            return True
        else:
            raise ValueError(
                f"Error: Unexpected observation shape {observation.shape} for MultiBinary "
                + f"environment, please use ({observation_space.n},) or "
                + f"(n_env, {observation_space.n}) for the observation shape."
            )
    else:
        raise ValueError(
            "Error: Cannot determine if the observation is vectorized " + f" with the space type {observation_space}."
        )


def safe_mean(arr: Union[np.ndarray, list, deque]) -> np.ndarray:
    """
    Compute the mean of an array if there is at least one element.
    For empty array, return NaN. It is used for logging only.

    :param arr:
    :return:
    """
    return np.nan if len(arr) == 0 else np.mean(arr)


def zip_strict(*iterables: Iterable) -> Iterable:
    r"""
    ``zip()`` function but enforces that iterables are of equal length.
    Raises ``ValueError`` if iterables not of equal length.
    Code inspired by Stackoverflow answer for question #32954486.

    :param \*iterables: iterables to ``zip()``
    """
    # As in Stackoverflow #32954486, use
    # new object for "empty" in case we have
    # Nones in iterable.
    sentinel = object()
    for combo in zip_longest(*iterables, fillvalue=sentinel):
        if sentinel in combo:
            raise ValueError("Iterables have different lengths")
        yield combo


def polyak_update(params: Iterable[th.nn.Parameter], target_params: Iterable[th.nn.Parameter], tau: float) -> None:
    """
    Perform a Polyak average update on ``target_params`` using ``params``:
    target parameters are slowly updated towards the main parameters.
    ``tau``, the soft update coefficient controls the interpolation:
    ``tau=1`` corresponds to copying the parameters to the target ones whereas nothing happens when ``tau=0``.
    The Polyak update is done in place, with ``no_grad``, and therefore does not create intermediate tensors,
    or a computation graph, reducing memory cost and improving performance.  We scale the target params
    by ``1-tau`` (in-place), add the new weights, scaled by ``tau`` and store the result of the sum in the target
    params (in place).
    See https://github.com/DLR-RM/stable-baselines3/issues/93

    :param params: parameters to use to update the target params
    :param target_params: parameters to update
    :param tau: the soft update coefficient ("Polyak update", between 0 and 1)
    """
    with th.no_grad():
        # zip does not raise an exception if length of parameters does not match.
        for param, target_param in zip_strict(params, target_params):
            target_param.data.mul_(1 - tau)
            th.add(target_param.data, param.data, alpha=tau, out=target_param.data)