text: import sys
  import gym
  import time
  import random
  import argparse
  import syft as sy
  import numpy as np
  from gym import wrappers, logger, spaces
  
  # Import environment classes
  import secret_breakout
  sys.path.append('secret_breakout')
  
  # Import agent classes
  from agents import RandomAgent, ReinforceAgent, ActorCriticAgent
  
  class WindyGridworldEnv(gym.Env):
      def __init__(self):
          self.height = 7
          self.width = 10
          self.num_actions = 4
          self.num_states = self.height * self.width
          self.action_space = spaces.Discrete(self.num_actions)
          self.observation_space = spaces.Tuple((
              spaces.Discrete(self.height),
              spaces.Discrete(self.width)
          ))
          self.moves = {
              # up
              0: (-1, 0),
              # right
              1: (0, 1),
              # down
              2: (1, 0),
              # left
              3: (0, -1),
          }
          # Begin in the start state
          self.reset()
  
      def __coords_to_idx(self, coords):
          return (self.width * coords[0]) + coords[1]
  
      def step(self, action):
          """Take a step in the windy gridworld."""
  
          # Update state due to wind
          if self.state[1] in (3, 4, 5, 8):
              self.state = self.state[0] - 1, self.state[1]
          elif self.state[1] in (6, 7):
              self.state = self.state[0] - 2, self.state[1]
  
          # Update the state due to action
          x, y = self.moves[action]
          self.state = self.state[0] + x, self.state[1] + y
  
          # Enforce the boundaries
          self.state = max(0, self.state[0]), max(0, self.state[1])
          self.state = (min(self.state[0], self.height - 1),
                        min(self.state[1], self.width - 1))
  
          # if reached the goal state
          if self.state == (3, 7):
              return self.__coords_to_idx(self.state), 0, True, {}
  
          return self.__coords_to_idx(self.state), -1, False, {}
  
      def reset(self):
          """Reset the state of the windy gridworld."""
          self.state = (3, 0)
          return self.__coords_to_idx(self.state)
  
  class SarsaAgent(object):
      def __init__(self, num_states, num_actions,
          epsilon=0.1, alpha=0.5, gamma=1.0):
  
          self.Q = sy.zeros(num_states, num_actions)
          self.epsilon = epsilon
          self.alpha = alpha
          self.gamma = gamma
          self.actions = range(num_actions)
  
      def act(self, state, reward, done):
          if random.random() < self.epsilon:
              action = random.choice(self.actions)
          else:
              # TODO(korymath): should break ties randomly
              value, action = self.Q[state, :].max(0)
              action = action[0]
          return action
  
      def update_agent(self, state, action, reward, next_state, next_action):
          # Calculate the td-error
          td_error = (reward + (self.gamma * self.Q[next_state, next_action] - self.Q[state, action]))
          # Update the action-value function
          self.Q[state, action] += self.alpha * td_error
          # Update the epsilon value to decay exploration
          self.epsilon = 0.99 * self.epsilon
  
  
  def main(args):
      # Make the environment.
      # env = gym.make(args.env_id)
      env = WindyGridworldEnv()
      env.seed(args.seed)
      state = env.reset()
  
      # Get the action and observation space from the environment.
      logger.debug('Action space vector length: {}'.format(env.action_space.n))
      # TODO: fix this so that it can be directly read from env.observation_space
      logger.debug('Observation space vector length: {}'.format(1))
      # logger.debug('Max episode steps: {}'.format(env.spec.max_episode_steps))
      max_episode_steps = int(1e9)
      logger.debug('Max episode steps: {}'.format(max_episode_steps))
  
      # Build the agent
      if args.agent_id == 'sarsa':
          agent = SarsaAgent(
              num_states=env.num_states,
              num_actions=env.num_actions,
              alpha=args.learning_rate,
              gamma=args.gamma)
  
      reward = 0
      done = False
  
      ep_rewards = []
      ep_start_time = time.time()
  
      for i_episode in range(args.max_episodes + 1):
          # Don't loop forever, add one to the env_max_steps
          # to make sure to take the final step
          state = env.reset()
          # get the action from the agent
          action = agent.act(state, reward, done)
          # keep track of the performance over the episode
          single_ep_cumulative_reward = 0
          for step in range(max_episode_steps):
              # perform the action in the environment
              next_state, reward, done, info = env.step(action)
              ####
              # sarsa specific updates
              next_action = agent.act(next_state, reward, done)
              if done:
                  break
              agent.update_agent(state, action, reward, next_state, next_action)
              state = next_state
              action = next_action
              #####
              # track the episode performance
              single_ep_cumulative_reward += reward
              if done:
                  break
  
          # add the accumulated reward to list of episode returns
          ep_rewards.append(single_ep_cumulative_reward)
  
          # update reporting times
          ep_report_time = round(time.time() - ep_start_time, 2)
          ep_start_time = time.time()
  
          if i_episode % args.log_interval == 0:
              logger.info('t(s): {}, ep: {}, R: {:.2f}, R_av_5: {:.2f}, i: {}'.format(
                  ep_report_time, i_episode, ep_rewards[-1], np.mean(ep_rewards[-5:]), info))
      env.close()
      return True
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(
          description='Arguments for OpenAI Gym Runs')
      parser.add_argument('--env_id',
                          default='SecretBreakout-v0',
                          help='Environment: (SecretBreakout-v0, CartPole-v0)')
      parser.add_argument('--agent_id',
                          default='sarsa',
                          help='Agent: (random, reinforce)')
      parser.add_argument('--log_interval',
                          type=int, default=10, metavar='N',
                          help='interval between status logs (default: 10)')
      parser.add_argument('--max_episodes',
                          type=int, default=1000,
                          help='maximum number of episodes to run')
      parser.add_argument('--verbose', action='store_true',
                          help='output verbose logging for steps')
      parser.add_argument('--random_action',
                          action='store_true',
                          help='Random policy for comparison')
      parser.add_argument('--gamma',
                          type=float, default=1.0, metavar='G',
                          help='discount factor (default: 1.0)')
      parser.add_argument('--learning_rate',
                          type=float, default=0.5,
                          help='learning rate (default: 0.5)')
      parser.add_argument('--seed',
                          type=int, metavar='N',
                          help='random seed')
      args = parser.parse_args()
      logger.set_level(logger.INFO)
  
      if args.verbose:
          logger.set_level(logger.DEBUG)
  
      # Set the random seed if defined
      if args.seed:
          random.seed(args.seed)
  
      # Run the training
      main(args)