content: import numpy as np
  
  
  class BaseStrategy(object):
      """
      Base class of exploration strategy.
      """
  
      def get_action(self, obs, policy):
  
          raise NotImplementedError
  
      def reset(self):
  
          pass
  
  
  class OUStrategy(BaseStrategy):
      """
      Ornstein-Uhlenbeck process: dxt = theta * (mu - xt) * dt + sigma * dWt
      where Wt denotes the Wiener process.
      """
  
      def __init__(self, env_spec, mu=0, theta=0.15, sigma=0.3):
  
          self.mu = mu
          self.theta = theta
          self.sigma = sigma
          self.action_space = env_spec.action_space
          self.state = np.ones(self.action_space.flat_dim) * self.mu
          
      def evolve_state(self):
  
          x = self.state
          dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(len(x))
          self.state = x + dx
  
          return self.state
  
      def reset(self):
  
          self.state = np.ones(self.action_space.flat_dim) * self.mu
  
      def get_action(self, obs, policy):
  
          # get_action accepts a 2D tensor with one row
      	obs = obs.reshape((1, -1))
          action = policy.get_action(obs)
          increment = self.evolve_state()
          
          return np.clip(action + increment, 
                         self.action_space.low, 
                         self.action_space.high)
  
  
  if __name__ == "__main__":
  
      class Env1(object):
  
          def __init__(self):
              self.action_space = Env2()
  
  
      class Env2(object):
  
          def __init__(self):
              self.flat_dim = 2
  
  
      env_spec = Env1()
      test = OUStrategy(env_spec)
  
      states = []
      for i in range(1000):
          states.append(test.evolve_state()[0])
      import matplotlib.pyplot as plt
  
      plt.plot(states)
      plt.show()
  
  
  