Pre-Training Problem

When I try to run the code below I get this error at the pretrain function: Error

  File "C:\Users\fabio\Desktop\wetransfer-08d028\Rope_ex_v1.5\RL_Training\behaviour_cloning.py", line 40, in <module>
    model.pretrain(dataset, n_epochs=1000)

  File "c:\users\fabio\desktop\virtual_env\env1\lib\site-packages\stable_baselines\common\base_class.py", line 346, in pretrain
    expert_obs, expert_actions = dataset.get_next_batch('train')

  File "c:\users\fabio\desktop\virtual_env\env1\lib\site-packages\stable_baselines\gail\dataset\dataset.py", line 152, in get_next_batch
    dataloader.start_process()

  File "c:\users\fabio\desktop\virtual_env\env1\lib\site-packages\stable_baselines\gail\dataset\dataset.py", line 231, in start_process
    self.process.start()

  File "C:\Python\Python37\lib\multiprocessing\process.py", line 112, in start
    self._popen = self._Popen(self)

  File "C:\Python\Python37\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)

  File "C:\Python\Python37\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)

  File "C:\Python\Python37\lib\multiprocessing\popen_spawn_win32.py", line 89, in __init__
    reduction.dump(process_obj, to_child)

  File "C:\Python\Python37\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)

PicklingError: Can't pickle <function rebuild_pipe_connection at 0x0000024B185C2168>: it's not the same object as multiprocessing.connection.rebuild_pipe_connection

Code

import gym
from stable_baselines.gail import generate_expert_traj
env = gym.make("CartPole-v1")
def dummy_expert(_obs):
    return env.action_space.sample()

generate_expert_traj(dummy_expert, 'expert_cartpole', env, n_episodes=10)

from stable_baselines import PPO2
from stable_baselines.gail import ExpertDataset

dataset = ExpertDataset(expert_path='expert_cartpole.npz',
                        traj_limitation=1, batch_size=128)

model = PPO2('MlpPolicy', 'CartPole-v1', verbose=1)
model.pretrain(dataset, n_epochs=1000)
model.learn(int(1e5))

env = model.get_env()
obs = env.reset()

reward_sum = 0.0
for _ in range(1000):
        action, _ = model.predict(obs)
        obs, reward, done, _ = env.step(action)
        reward_sum += reward
        env.render()
        if done:
                print(reward_sum)
                reward_sum = 0.0
                obs = env.reset()

env.close()

[Miffyli]

The full traceback of all processes tells the issue: It is trying to load file expert_cartpole.npz when creating ExpertDataset, but data is stored in dummy_expert_cartpole.npz. Fixing this fixes the issue.

[FabioPINO]

I tried but I get the same error as in the main question.

import gym

from stable_baselines.gail import generate_expert_traj
from stable_baselines import PPO2
from stable_baselines.gail import ExpertDataset

if __name__ == "__main__":

    env = gym.make("CartPole-v1")
    # Here the expert is a random agent
    # but it can be any python function, e.g. a PID controller
    def dummy_expert(_obs):
        """
        Random agent. It samples actions randomly
        from the action space of the environment.
    
        :param _obs: (np.ndarray) Current observation
        :return: (np.ndarray) action taken by the expert
        """
        return env.action_space.sample()
    # Data will be saved in a numpy archive named `expert_cartpole.npz`
    # when using something different than an RL expert,
    # you must pass the environment object explicitly
    generate_expert_traj(dummy_expert, 'expert_cartpole', env, n_episodes=10)
    
    
    # Using only one expert trajectory
    # you can specify `traj_limitation=-1` for using the whole dataset
    dataset = ExpertDataset(expert_path='expert_cartpole.npz',
                            traj_limitation=1, batch_size=128)
    
    model = PPO2('MlpPolicy', 'CartPole-v1', verbose=1)
    # Pretrain the PPO2 model
    model.pretrain(dataset, n_epochs=1)
    
    # As an option, you can train the RL agent
    # model.learn(int(1e5))
    
    # Test the pre-trained model
    env = model.get_env()
    obs = env.reset()
    
    reward_sum = 0.0
    for _ in range(1000):
            action, _ = model.predict(obs)
            obs, reward, done, _ = env.step(action)
            reward_sum += reward
            env.render()
            if done:
                    print(reward_sum)
                    reward_sum = 0.0
                    obs = env.reset()
    
    env.close()