stable-baselines3学习之Tensorboard

stable-baselines3学习之Tensorboard系列

1.基本用法

要使用stable-baselines3的 Tensorboard，您只需将日志文件夹的位置传递给 RL 的agent：

from stable_baselines3 import A2C

model = A2C('MlpPolicy', 'CartPole-v1', verbose=1, tensorboard_log="./a2c_cartpole_tensorboard/")
model.learn(total_timesteps=10_000)

您还可以在训练时定义自定义日志名称（默认为算法名称）

from stable_baselines3 import A2C

model = A2C('MlpPolicy', 'CartPole-v1', verbose=1, tensorboard_log="./a2c_cartpole_tensorboard/")
model.learn(total_timesteps=10_000, tb_log_name="first_run")
# Pass reset_num_timesteps=False to continue the training curve in tensorboard
# By default, it will create a new curve
model.learn(total_timesteps=10_000, tb_log_name="second_run", reset_num_timesteps=False)
model.learn(total_timesteps=10_000, tb_log_name="third_run", reset_num_timesteps=False)

调用 learn 函数后，您可以使用以下 bash 命令在训练期间或之后监控 RL agent：

tensorboard --logdir ./a2c_cartpole_tensorboard/

注：要在该项目文件路径下运行这条命令

比如：

2.Logging More Values

使用callback可以容易的记录更多日志用Tensorboard，这里有一个简单的例子去记录额外的tensor和任意的scalar值：

import numpy as np

from stable_baselines3 import SAC
from stable_baselines3.common.callbacks import BaseCallback

model = SAC("MlpPolicy", "Pendulum-v0", tensorboard_log="./tmp/sac0/", verbose=1)


class TensorboardCallback(BaseCallback):
    """
    Custom callback for plotting additional values in tensorboard.
    """

    def __init__(self, verbose=0):
        super(TensorboardCallback, self).__init__(verbose)

    def _on_step(self) -> bool:
        # Log scalar value (here a random variable)
        value = np.random.random()
        self.logger.record('random_value', value)
        return True


model.learn(50000, callback=TensorboardCallback())

tensorboard --logdir ./tmp/sac0/

3.Logging Images

TensorBoard 支持定期记录图像数据，这有助于在训练期间的各个阶段评估agent。

以下是如何定期将图像渲染到 TensorBoard 的示例：

from stable_baselines3 import SAC
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.logger import Image

model = SAC("MlpPolicy", "Pendulum-v0", tensorboard_log="./tmp/sac1/", verbose=1)


class ImageRecorderCallback(BaseCallback):
    def __init__(self, verbose=0):
        super(ImageRecorderCallback, self).__init__(verbose)

    def _on_step(self):
        image = self.training_env.render(mode="rgb_array")
        # "HWC" specify the dataformat of the image, here channel last
        # (H for height, W for width, C for channel)
        # See https://pytorch.org/docs/stable/tensorboard.html
        # for supported formats
        self.logger.record("trajectory/image", Image(image, "HWC"), exclude=("stdout", "log", "json", "csv"))
        return True


model.learn(50000, callback=ImageRecorderCallback())

tensorboard --logdir ./tmp/sac1/

4.Logging Figures/Plots

TensorBoard 支持定期记录使用 matplotlib 创建的图形/绘图，这有助于在训练期间评估各个阶段的agent。

以下是如何在 TensorBoard 中定期存储绘图的示例：

import numpy as np
import matplotlib.pyplot as plt

from stable_baselines3 import SAC
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.logger import Figure

model = SAC("MlpPolicy", "Pendulum-v0", tensorboard_log="./tmp/sac2/", verbose=1)


class FigureRecorderCallback(BaseCallback):
    def __init__(self, verbose=0):
        super(FigureRecorderCallback, self).__init__(verbose)

    def _on_step(self):
        # Plot values (here a random variable)
        figure = plt.figure()
        figure.add_subplot().plot(np.random.random(3))
        # Close the figure after logging it
        self.logger.record("trajectory/figure", Figure(figure, close=True), exclude=("stdout", "log", "json", "csv"))
        plt.close()
        return True


model.learn(50000, callback=FigureRecorderCallback())

tensorboard --logdir ./tmp/sac1/

5.Logging Videos

TensorBoard 支持定期记录视频数据，这有助于在训练期间评估各个阶段的agent。

以下是如何显示一个episode并将生成的视频定期记录到 TensorBoard 的示例：

注：需安装moviepy包

from typing import Any, Dict

import gym
import torch as th

from stable_baselines3 import A2C
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.logger import Video


class VideoRecorderCallback(BaseCallback):
    def __init__(self, eval_env: gym.Env, render_freq: int, n_eval_episodes: int = 1, deterministic: bool = True):
        """
        Records a video of an agent's trajectory traversing ``eval_env`` and logs it to TensorBoard

        :param eval_env: A gym environment from which the trajectory is recorded
        :param render_freq: Render the agent's trajectory every eval_freq call of the callback.
        :param n_eval_episodes: Number of episodes to render
        :param deterministic: Whether to use deterministic or stochastic policy
        """
        super().__init__()
        self._eval_env = eval_env
        self._render_freq = render_freq
        self._n_eval_episodes = n_eval_episodes
        self._deterministic = deterministic

    def _on_step(self) -> bool:
        if self.n_calls % self._render_freq == 0:
            screens = []

            def grab_screens(_locals: Dict[str, Any], _globals: Dict[str, Any]) -> None:
                """
                Renders the environment in its current state, recording the screen in the captured `screens` list

                :param _locals: A dictionary containing all local variables of the callback's scope
                :param _globals: A dictionary containing all global variables of the callback's scope
                """
                screen = self._eval_env.render(mode="rgb_array")
                # PyTorch uses CxHxW vs HxWxC gym (and tensorflow) image convention
                screens.append(screen.transpose(2, 0, 1))

            evaluate_policy(
                self.model,
                self._eval_env,
                callback=grab_screens,
                n_eval_episodes=self._n_eval_episodes,
                deterministic=self._deterministic,
            )
            self.logger.record(
                "trajectory/video",
                Video(th.ByteTensor([screens]), fps=40),
                exclude=("stdout", "log", "json", "csv"),
            )
        return True


model = A2C("MlpPolicy", "CartPole-v1", tensorboard_log="./tmp/runs/", verbose=1)
video_recorder = VideoRecorderCallback(gym.make("CartPole-v1"), render_freq=5000)
model.learn(total_timesteps=int(5e4), callback=video_recorder)

tensorboard --logdir ./tmp/runs/