text: from random import randint
  import torch.nn as nn
  import torch.optim as optim
  from flare.algorithm_zoo.simple_algorithms import SimpleAC
  from flare.framework.manager import Manager
  from flare.model_zoo.simple_models import SimpleRNNModelAC
  from flare.agent_zoo.simple_rl_agents import SimpleRNNRLAgent
  from flare.framework.agent import OnlineHelper
  from flare.env_zoo.gym_env import GymEnv
  
  if __name__ == '__main__':
      """
      A demo of how to run a simple RL experiment
      """
      game = "CartPole-v0"
  
      num_agents = 16
      num_games = 8000
  
      env = GymEnv(game)
      state_shape = env.observation_dims()["sensor"]
      num_actions = env.action_dims()["action"]
  
      # 1. Spawn one agent for each instance of environment.
      #    Agent's behavior depends on the actual algorithm being used. Since we
      #    are using SimpleAC, a proper type of Agent is SimpleRLAgent.
      reward_shaping_f = lambda x: x / 100.0
      agents = []
      for _ in range(num_agents):
          agent = SimpleRNNRLAgent(num_games, reward_shaping_f=reward_shaping_f)
          agent.set_env(GymEnv, game_name=game)
          agents.append(agent)
  
      # 2. Construct the network and specify the algorithm.
      #    Here we use a small MLP and apply the Actor-Critic algorithm
      hidden_size = 128
      mlp = nn.Sequential(
          nn.Linear(state_shape[0], hidden_size),
          nn.ReLU(), nn.Linear(hidden_size, hidden_size), nn.ReLU())
  
      alg = SimpleAC(
          model=SimpleRNNModelAC(
              dims=state_shape, num_actions=num_actions, perception_net=mlp),
          optim=(optim.RMSprop, dict(lr=1e-4)),
          ntd=True)
  
      # 3. Specify the settings for learning: the algorithm to use (SimpleAC
      # in this case), data sampling strategy (OnlineHelper here) and other
      # settings used by ComputationTask.
      ct_settings = {
          "RL": dict(
              alg=alg,
              # sampling
              agent_helper=OnlineHelper,
              sample_interval=8,
              num_agents=num_agents)
      }
  
      # 4. Create Manager that handles the running of the whole pipeline
      manager = Manager(ct_settings)
      manager.add_agents(agents)
      manager.start()
  