old_contents: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  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()
  
  
  
new_contents: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  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()
  
subject: Fix TabError for Python 3 (#11669)