[Question] AttributeError: 'numpy.random._generator.Generator' object has no attribute 'randint'

So I was working on my atari breakout project and whenever I do evaluate policy I get this error:

AttributeError                            Traceback (most recent call last)
Input In [21], in <cell line: 1>()
----> 1 evaluate_policy(model, env, n_eval_episodes=10)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\stable_baselines3\common\evaluation.py:82, in evaluate_policy(model, env, n_eval_episodes, deterministic, render, callback, reward_threshold, return_episode_rewards, warn)
     80 current_rewards = np.zeros(n_envs)
     81 current_lengths = np.zeros(n_envs, dtype="int")
---> 82 observations = env.reset()
     83 states = None
     84 episode_starts = np.ones((env.num_envs,), dtype=bool)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\stable_baselines3\common\vec_env\vec_frame_stack.py:58, in VecFrameStack.reset(self)
     54 def reset(self) -> Union[np.ndarray, Dict[str, np.ndarray]]:
     55     """
     56     Reset all environments
     57     """
---> 58     observation = self.venv.reset()  # pytype:disable=annotation-type-mismatch
     60     observation = self.stackedobs.reset(observation)
     61     return observation

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\stable_baselines3\common\vec_env\dummy_vec_env.py:61, in DummyVecEnv.reset(self)
     59 def reset(self) -> VecEnvObs:
     60     for env_idx in range(self.num_envs):
---> 61         obs = self.envs[env_idx].reset()
     62         self._save_obs(env_idx, obs)
     63     return self._obs_from_buf()

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\gym\core.py:292, in Wrapper.reset(self, **kwargs)
    291 def reset(self, **kwargs):
--> 292     return self.env.reset(**kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\gym\core.py:333, in RewardWrapper.reset(self, **kwargs)
    332 def reset(self, **kwargs):
--> 333     return self.env.reset(**kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\gym\core.py:319, in ObservationWrapper.reset(self, **kwargs)
    318 def reset(self, **kwargs):
--> 319     observation = self.env.reset(**kwargs)
    320     return self.observation(observation)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\stable_baselines3\common\atari_wrappers.py:59, in FireResetEnv.reset(self, **kwargs)
     58 def reset(self, **kwargs) -> np.ndarray:
---> 59     self.env.reset(**kwargs)
     60     obs, _, done, _ = self.env.step(1)
     61     if done:

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\stable_baselines3\common\atari_wrappers.py:106, in EpisodicLifeEnv.reset(self, **kwargs)
     97 """
     98 Calls the Gym environment reset, only when lives are exhausted.
     99 This way all states are still reachable even though lives are episodic,
   (...)
    103 :return: the first observation of the environment
    104 """
    105 if self.was_real_done:
--> 106     obs = self.env.reset(**kwargs)
    107 else:
    108     # no-op step to advance from terminal/lost life state
    109     obs, _, _, _ = self.env.step(0)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\stable_baselines3\common\atari_wrappers.py:154, in MaxAndSkipEnv.reset(self, **kwargs)
    153 def reset(self, **kwargs) -> GymObs:
--> 154     return self.env.reset(**kwargs)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\stable_baselines3\common\atari_wrappers.py:36, in NoopResetEnv.reset(self, **kwargs)
     34     noops = self.override_num_noops
     35 else:
---> 36     noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
     37 assert noops > 0
     38 obs = np.zeros(0)

AttributeError: 'numpy.random._generator.Generator' object has no attribute 'randint'

---

Here is my code:

import gym
from stable_baselines3 import A2C
from stable_baselines3.common.vec_env import VecFrameStack
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.env_util import make_vec_env

import os
from gym.utils import play
from stable_baselines3.ddpg.policies import CnnPolicy

from ale_py import ALEInterface
from ale_py.roms import Breakout

ale = ALEInterface()


ale.loadROM(Breakout)
environment_name = env = gym.make('ALE/Breakout-v5', render_mode='human')
env = make_atari_env('Breakout-v0', n_envs=4, seed=0)
env = VecFrameStack(env, n_stack=4)
log_path = os.path.join('Training', 'Logs')
model = A2C('CnnPolicy', environment_name, verbose=1, tensorboard_log=log_path)
a2c_path = os.path.join('Training','Logs', 'A2C_300k_model')
model.save(a2c_path)
del model
model = A2C.load(a2c_path, env)
evaluate_policy(model, env, n_eval_episodes=10)

---

Please help. Thanks.

[qgallouedec]

Next time, please fill in the issue template. This way you would have realized that this question has already been asked and duplicated: #875

[qgallouedec]

Please open a new issue, as it is not related to your original topic.