SubProcVecEnv raises ConnectionResetError: [Errno 104] Connection reset by peer

Traceback (most recent call last):

  File "<ipython-input-2-9712559f6750>", line 3, in <module>
    env = SubprocVecEnv([foo(game,state) for _ in range(num_cpu)])

  File "/home/chryskan/anaconda3/lib/python3.7/site-packages/stable_baselines/common/vec_env/subproc_vec_env.py", line 96, in __init__
    observation_space, action_space = self.remotes[0].recv()

  File "/home/chryskan/anaconda3/lib/python3.7/multiprocessing/connection.py", line 250, in recv
    buf = self._recv_bytes()

  File "/home/chryskan/anaconda3/lib/python3.7/multiprocessing/connection.py", line 407, in _recv_bytes
    buf = self._recv(4)

  File "/home/chryskan/anaconda3/lib/python3.7/multiprocessing/connection.py", line 379, in _recv
    chunk = read(handle, remaining)

ConnectionResetError: [Errno 104] Connection reset by peer

from stable_baselines.common.policies import MlpPolicy, CnnPolicy
from stable_baselines.common import make_vec_env
from stable_baselines import PPO2
from my_wrappers import wrap_sonic, make_sonic
from stable_baselines.common.env_checker import check_env
from stable_baselines.common.vec_env import SubprocVecEnv`

def foo(game, state):
        def bar():
            return wrap_sonic(make_sonic(game, state), episode_life=False, 
                  frame_stack=True, scale=True,
                  reward_scale = True, sonic_actions=True, 
                  max_x_reward=True)
        return bar

game , state = "SonicTheHedgehog-Genesis", "GreenHillZone.Act1"
num_cpu = 4
env = SubprocVecEnv([foo(game,state) for _ in range(num_cpu)])

model = PPO2(CnnPolicy, env, verbose=1)
model.learn(total_timesteps=1000_000)

import numpy as np
from collections import deque
import gym
from gym import spaces
import cv2
from retro_contest.local import make

height, width = 84, 84

class ActionsDiscretizer(gym.ActionWrapper):
    def __init__(self, env):
        super(ActionsDiscretizer, self).__init__(env)
        buttons = ["B", "A", "MODE", "START", "UP", "DOWN", "LEFT", 
                   "RIGHT", "C", "Y", "X", "Z"]
        actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], 
                   ['RIGHT', 'DOWN'], ['DOWN'],
                   ['DOWN', 'B'], ['B']]
        self._actions = []

        for action in actions:
            arr = np.array([False] * 12)
            for button in action:
                arr[buttons.index(button)] = True
            self._actions.append(arr)
        self.action_space = gym.spaces.Discrete(len(self._actions))

    def action(self, a):
        return self._actions[a].copy()

class RewardScaler(gym.RewardWrapper):
    """
    Bring rewards to a reasonable scale for PPO.
    This is incredibly important and effects performance
    drastically.
    """
    def reward(self, reward):
        return reward * 0.01

class AllowBacktracking(gym.Wrapper):
    """
    Use deltas in max(X) as the reward, rather than deltas
    in X. This way, agents are not discouraged too heavily
    from exploring backwards if there is no way to advance
    head-on in the level.
    """
    def __init__(self, env):
        super(AllowBacktracking, self).__init__(env)
        self._cur_x = 0
        self._max_x = 0

    def reset(self, **kwargs):
        self._cur_x = 0
        self._max_x = 0
        return self.env.reset(**kwargs)

    def step(self, action):
        obs, rew, done, info = self.env.step(action)
        self._cur_x += rew
        rew = max(0, self._cur_x - self._max_x)
        self._max_x = max(self._max_x, self._cur_x)
        return obs, rew, done, info

class NoopResetEnvSonic(gym.Wrapper):
    def __init__(self, env, noop_max=30):
        """Sample initial states by taking random number of no-ops on reset.
        No-op is assumed to be action 0.
        """
        gym.Wrapper.__init__(self,env)
        self.noop_max = noop_max
        self.override_num_noops = None
        self.noop_action = np.zeros(env.action_space.n)
    
    def reset(self, **kwargs):
         """ Do no-op action for a number of steps in [1, noop_max]."""
         self.env.reset(**kwargs)
         if self.override_num_noops is not None:
             noops = self.override_num_noops
         else:
            noops = self.unwrapped.np_random.randint(1, self.noop_max+1)
         assert noops > 0
         obs = None
         for _ in range(noops):
             obs, _, done, _ = self.env.step(self.noop_action)
             if done:
                 obs = self.env.reset(**kwargs)
         return obs
     
class MaxAndSkipEnv(gym.Wrapper):
    def __init__(self, env, skip=4):
        """Return only every `skip`-th frame"""
        gym.Wrapper.__init__(self, env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = np.zeros((2,)+env.observation_space.shape, dtype='uint8')
        self._skip = skip
    
    def step(self, action):
        """Repeat action, sum reward, and max over last observations."""        
        total_reward = 0.0
        done = None
        for i in range(self._skip):
            obs, reward, done, info = self.env.step(action)
            if i==self._skip - 2: self._obs_buffer[0] = obs
            if i==self._skip - 1: self._obs_buffer[1] = obs
            total_reward += reward
            if done:
                break
        # Note that the observation on the done=True frame
        # doesn't matter
        max_frame = self._obs_buffer.max(axis=0)

        return max_frame, total_reward, done, info
     
class FireResetEnv(gym.Wrapper):
    def __init__(self, env):
        """Take action on reset for environments that are fixed until firing."""
        gym.Wrapper.__init__(self, env)
        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
        assert len(env.unwrapped.get_action_meanings()) >= 3
    
    def reset(self, **kwargs):
        self.env.reset(**kwargs)
        obs, _, done, _ = self.env.step(1)
        if done:
            self.env.reset(**kwargs)
        obs, _, done, _ = self.env.step(2)
        if done:
            self.env.reset(**kwargs)
        return obs

class EpisodicLifeEnv(gym.Wrapper):
    def __init__(self, env):
        """Make end-of-life == end-of-episode, but only reset on true game over.
        Done by DeepMind for the DQN and co. since it helps value estimation.
        """
        gym.Wrapper.__init__(self, env)
        self.lives = 0
        self.was_real_done  = True
        
    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        self.was_real_done = done
        # check current lives, make loss of life terminal,
        # then update lives to handle bonus lives
        lives = self.env.unwrapped.ale.lives()
        if lives < self.lives and lives > 0:
            # for Qbert somtimes we stay in lives == 0 condtion for a few frames
            # so its important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            done = True
        self.lives = lives
        return obs, reward, done, info
    
    def reset(self, **kwargs):
        """Reset only when lives are exhausted.
        This way all states are still reachable even though lives are episodic,
        and the learner need not know about any of this behind-the-scenes.
        """
        if self.was_real_done:
            obs = self.env.reset(**kwargs)
        else:
            # no-op step to advance from terminal/lost life state
            obs ,_, _, _ = self.env.step(0)    
        self.lives = self.env.unwrapped.ale.lives()
        return obs

class ClipRewardEnv(gym.RewardWrapper):
    def reward(self, reward):
        """Bin reward to {+1, 0, -1} by its sign."""
        return np.sign(reward)

class WarpFrame(gym.ObservationWrapper):
    def __init__(self, env):
        """Warp frames to 84x84 as done in the Nature paper and later work."""
        gym.ObservationWrapper.__init__(self, env)
        self.width = width
        self.height = height
        self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1), dtype=np.uint8)

    def observation(self, frame):
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
        return frame[:, :, None]

class FrameStack(gym.Wrapper):
    def __init__(self, env, k):
        """Stack k last frames.
        Returns lazy array, which is much more memory efficient.
        See Also
        --------
        baselines.common.atari_wrappers.LazyFrames
        """
        gym.Wrapper.__init__(self, env)
        self.k = k
        self.frames = deque([], maxlen=k)
        shp = env.observation_space.shape
        self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=np.uint8)

    def reset(self):
        ob = self.env.reset()
        for _ in range(self.k):
            self.frames.append(ob)
        return self._get_ob()

    def step(self, action):
        ob, reward, done, info = self.env.step(action)
        self.frames.append(ob)
        return self._get_ob(), reward, done, info

    def _get_ob(self):
        assert len(self.frames) == self.k
        return np.reshape(self.frames, newshape=self.observation_space.shape)

class ScaledFloatFrame(gym.ObservationWrapper):
    def observation(self, observation):
        # careful! This undoes the memory optimization, use
        # with smaller replay buffers only.
        return np.array(observation).astype(np.float32) / 255.0
    
class LazyFrames(object):
    def __init__(self, frames):
        """This object ensures that common frames between the observations are only stored once.
        It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
        buffers.
        This object should only be converted to numpy array before being passed to the model.
        You'd not believe how complex the previous solution was."""
        self._frames = frames

    def __array__(self, dtype=None):
        out = np.concatenate(self._frames, axis=2)
        if dtype is not None:
            out = out.astype(dtype)
        return out

def make_sonic(game, state):
    env = make(game=game, state=state)
    #env = retrowrapper.RetroWrapper(game=game, state=state)
    env = NoopResetEnvSonic(env, noop_max=30)
    env = MaxAndSkipEnv(env, skip=4)
    return env

def wrap_sonic(env, episode_life=True, clip_rewards=False, 
                  frame_stack=False, scale=False, pytorch_img=False,
                  reward_scale = True, sonic_actions=True, max_x_reward=True):
    if episode_life:
        env = EpisodicLifeEnv(env)
    
    key = 'get_action_meanings'
    if hasattr(env.unwrapped, key):
        meanings = env.unwrapped.get_action_meanings()
    else:
        meanings = [env.unwrapped.get_action_meaning(np.zeros(a)) 
                        for a in range(env.action_space.n)]
    if 'FIRE' in meanings:
        env = FireResetEnv(env)
    env = WarpFrame(env)
    if scale:
        env = ScaledFloatFrame(env)
    if max_x_reward:
        env=AllowBacktracking(env)
    if sonic_actions:
        env=ActionsDiscretizer(env)
    if clip_rewards:
        env = ClipRewardEnv(env)
    if reward_scale:
        env = RewardScaler(env)
    if frame_stack:
        env = FrameStack(env, 4)
    return env