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20 hours ago •
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SubProcVecEnv raises ConnectionResetError: [Errno 104] Connection reset by peer
I'm trying to run the following code and test PPO with Sonic the hedghehog, running it in parralel with SubProcVecEnv Unfortunately I run in the following error:
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
This is the code:
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)
And the my_wrappers module
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
I was facing similar issues, but I solved it by removing verbose. Try removing verbose =1 , from model = PPO2(CnnPolicy, env, verbose=1).
/root/miniconda3/lib/python3.7/multiprocessing/connection.py:379: ConnectionResetError
----------------------------------------------------------------------------- Captured stderr call ------------------------------------------------------------------------------
Process SpawnProcess-2:
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/root/miniconda3/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "/root/virtualenv/venv/baselines/baselines/common/vec_env/subproc_vec_env.py", line 15, in worker
envs = [env_fn_wrapper() for env_fn_wrapper in env_fn_wrappers.x]
File "/root/virtualenv/venv/baselines/baselines/common/vec_env/subproc_vec_env.py", line 15, in <listcomp>
envs = [env_fn_wrapper() for env_fn_wrapper in env_fn_wrappers.x]
File "/root/virtualenv/venv/baselines/baselines/common/tests/test_env_after_learn.py", line 15, in make_env
env = gym.make('CartPole-v1' if algo == 'acktr' else 'PongNoFrameskip-v4')
File "/root/miniconda3/lib/python3.7/site-packages/gym/envs/registration.py", line 156, in make
return registry.make(id, **kwargs)
File "/root/miniconda3/lib/python3.7/site-packages/gym/envs/registration.py", line 101, in make
env = spec.make(**kwargs)
File "/root/miniconda3/lib/python3.7/site-packages/gym/envs/registration.py", line 73, in make
env = cls(**_kwargs)
File "/root/miniconda3/lib/python3.7/site-packages/gym/envs/atari/atari_env.py", line 49, in __init__
self.game_path = atari_py.get_game_path(game)
File "/root/miniconda3/lib/python3.7/site-packages/atari_py/games.py", line 20, in get_game_path
raise Exception('ROM is missing for %s, see https://github.com/openai/atari-py#roms for instructions' % (game_name,))
Exception: ROM is missing for pong, see https://github.com/openai/atari-py#roms for instructions
__________________________________________________________________________ test_env_after_learn[deepq] __________________________________________________________________________
algo = 'deepq'
@pytest.mark.parametrize('algo', algos)
def test_env_after_learn(algo):
def make_env():
# acktr requires too much RAM, fails on travis
env = gym.make('CartPole-v1' if algo == 'acktr' else 'PongNoFrameskip-v4')
return env
make_session(make_default=True, graph=tf.Graph())
> env = SubprocVecEnv([make_env])
baselines/common/tests/test_env_after_learn.py:19:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
baselines/common/vec_env/subproc_vec_env.py:71: in __init__
observation_space, action_space, self.spec = self.remotes[0].recv().x
/root/miniconda3/lib/python3.7/multiprocessing/connection.py:250: in recv
buf = self._recv_bytes()
/root/miniconda3/lib/python3.7/multiprocessing/connection.py:407: in _recv_bytes
buf = self._recv(4)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <multiprocessing.connection.Connection object at 0x7f4be43769b0>, size = 4, read = <built-in function read>
def _recv(self, size, read=_read):
buf = io.BytesIO()
handle = self._handle
remaining = size
while remaining > 0:
> chunk = read(handle, remaining)
E ConnectionResetError: [Errno 104] Connection reset by peer
/root/miniconda3/lib/python3.7/multiprocessing/connection.py:379: ConnectionResetError
----------------------------------------------------------------------------- Captured stderr call
I noticed this error:
raise Exception('ROM is missing for %s, see https://github.com/openai/atari-py#roms for instructions' % (game_name,))
When I solved this error, ConnectionResetError: [Errno 104] Connection reset by peer disappeared.But there is one error left
===================================================== 46 failed, 49 passed, 31 skipped, 1111 warnings in 110.05s (0:01:50) ======================================================
The error becomes:
====================================================== 1 failed, 94 passed, 31 skipped, 6368 warnings in 269.86s (0:04:29) ======================================================
baselines/common/tests/test_doc_examples.py:20: in <lambda>
venv = DummyVecEnv([lambda: cmd_util.make_mujoco_env('Reacher-v2', seed=0)])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
env_id = 'Reacher-v2', seed = 0, reward_scale = 1.0
def make_mujoco_env(env_id, seed, reward_scale=1.0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
> rank = MPI.COMM_WORLD.Get_rank()
E AttributeError: 'NoneType' object has no attribute 'COMM_WORLD'
baselines/common/cmd_util.py:112: AttributeError
My environment(for your information):
┌──(root💀agi)-[~]
└─# uname -a
Linux agi 5.14.0-kali4-amd64 #1 SMP Debian 5.14.16-1kali1 (2021-11-05) x86_64 GNU/Linux
┌──(venv)─(root💀agi)-[~/virtualenv/venv/baselines]
└─# pip list 130 ⨯
Package Version Location
---------------------- ---------- -------------------------------
absl-py 1.0.0
asn1crypto 0.24.0
astor 0.8.1
atari-py 0.2.9
attrs 21.2.0
baselines 0.1.6 /root/virtualenv/venv/baselines
box2d-py 2.3.8
cached-property 1.5.2
certifi 2021.10.8
cffi 1.12.2
chardet 3.0.4
click 8.0.3
cloudpickle 1.2.2
conda 4.10.3
conda-package-handling 1.7.3
cryptography 2.6.1
cycler 0.11.0
Cython 0.29.26
filelock 3.4.0
fonttools 4.28.5
future 0.18.2
gast 0.5.3
glfw 2.5.0
google-pasta 0.2.0
grpcio 1.43.0
gym 0.15.7
h5py 3.6.0
idna 2.8
imageio 2.13.4
importlib-metadata 4.10.0
iniconfig 1.1.1
joblib 1.1.0
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
kiwisolver 1.3.2
lockfile 0.12.2
Markdown 3.3.6
matplotlib 3.5.1
mujoco-py 1.50.1.68
numpy 1.21.5
opencv-python 4.5.4.60
packaging 21.3
pandas 1.3.5
Pillow 8.4.0
pip 19.0.3
pluggy 1.0.0
protobuf 3.19.1
py 1.11.0
pycosat 0.6.3
pycparser 2.19
pyglet 1.5.0
pyOpenSSL 19.0.0
pyparsing 3.0.6
PySocks 1.6.8
pytest 6.2.5
python-dateutil 2.8.2
pytz 2021.3
requests 2.21.0
ruamel-yaml 0.15.46
scipy 1.7.3
setuptools 41.0.0
six 1.12.0
tensorboard 1.14.0
tensorflow 1.14.0
tensorflow-estimator 1.14.0
termcolor 1.1.0
toml 0.10.2
tqdm 4.62.3
typing-extensions 4.0.1
urllib3 1.24.1
Werkzeug 2.0.2
wheel 0.33.1
wrapt 1.13.3
youtube-dl 2021.12.17
zipp 3.6.0
