• Stable Baselines/用户向导/Tensorboard集成

  Stable Baselines官方文档中文版 Github CSDN 尝试翻译官方文档，水平有限，如有错误万望指正

• 初阶用法

  与RL baselines一起使用Tensorboard，你只需为RL智体简单定义一个log位置即可：

  import gym
  from stable_baselines import A2C
  model = A2C('MlpPolicy', 'CartPole-v1', verbose=1, tensorboard_log="./a2c_cartpole_tensorboard/")
  model.learn(total_timesteps=10000)

  或者加载现存模型之后（日志路径默认未保存）：

  import gym
  from stable_baselines.common.vec_env import DummyVecEnv
  from stable_baselines import A2C
  env = gym.make('CartPole-v1')
  env = DummyVecEnv([lambda: env])  # The algorithms require a vectorized environment to run
  model = A2C.load("./a2c_cartpole.pkl", env=env, tensorboard_log="./a2c_cartpole_tensorboard/")
  model.learn(total_timesteps=10000)

  学习函数一经调用，你可以在训练过程中或训练完成后，通过以下命令监视RL智体：

  tensorboard --logdir ./a2c_cartpole_tensorboard/

  也可添加过去的日志文件夹：

  tensorboard --logdir ./a2c_cartpole_tensorboard/;./ppo2_cartpole_tensorboard/

  它可展示诸多信息：模型图表、事件奖励、模型损失、观察、其他一些模型的特有参数。

  

  

  

• 日志更新

  使用回调函数，你用TensorBoard可轻松更新日志。这里是一个如何更新额外张量或随机标量的简单案例：

  ```python import tensorflow as tf import numpy as np

  from stable_baselines import SAC

  model = SAC(“MlpPolicy”, “Pendulum-v0”, tensorboard_log=”/tmp/sac/“, verbose=1)

  Define a new property to avoid global variable

  model.is_tb_set = False

def callback(locals, globals): self = locals[‘self’]

  # Log additional tensor
  if not self_.is_tb_set:
      with self_.graph.as_default():
          tf.summary.scalar('value_target', tf.reduce_mean(self_.value_target))
          self_.summary = tf.summary.merge_all()
      self_.is_tb_set = True
  # Log scalar value (here a random variable)
  value = np.random.random()
  summary = tf.Summary(value=[tf.Summary.Value(tag='random_value', simple_value=value)])
  locals_['writer'].add_summary(summary, self_.num_timesteps)
  return True

model.learn(50000, callback=callback)

- ## 主干集成
  终端展示的所有信息（默认日志）也可在*tensorboard*展示。为此，你需要定义几个环境变量：
  ```python
  # formats are comma-separated, but for tensorboard you only need the last one
  # stdout -> terminal
  export OPENAI_LOG_FORMAT='stdout,log,csv,tensorboard'
  export OPENAI_LOGDIR=path/to/tensorboard/data

用下述命令配置日志程序：

  from stable_baselines.logger import configure
  configure()

然后启动tensorboard：

  tensorboard --logdir=$OPENAI_LOGDIR