PokemonRedExperiments
PokemonRedExperiments copied to clipboard
error randomly happens while training
step: 20050 event: 4.00 level: 44.00 heal: 4.31 op_lvl: 0.00 dead: -0.40 badge: 0.00 explore: 59.52 sum: 111.43Process SpawnProcess-10:
Traceback (most recent call last):
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\multiprocessing\process.py", line 314, in _bootstrap
self.run()
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\multiprocessing\process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\common\vec_env\subproc_vec_env.py", line 35, in _worker
observation, reward, terminated, truncated, info = env.step(data)
File "C:\Users\jerem\OneDrive\Projects\python\PokemonRedExperiments\baselines\red_gym_env.py", line 225, in step
self.save_and_print_info(step_limit_reached, obs_memory)
File "C:\Users\jerem\OneDrive\Projects\python\PokemonRedExperiments\baselines\red_gym_env.py", line 402, in save_and_print_info
plt.imsave(
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\matplotlib\pyplot.py", line 2200, in imsave
return matplotlib.image.imsave(fname, arr, **kwargs)
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\matplotlib\image.py", line 1689, in imsave
image.save(fname, **pil_kwargs)
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\PIL\Image.py", line 2429, in save
fp = builtins.open(filename, "w+b")
PermissionError: [Errno 13] Permission denied: 'session_b890c2bc\\curframe_4b3a833e.jpeg'
Traceback (most recent call last):
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\multiprocessing\connection.py", line 312, in _recv_bytes
nread, err = ov.GetOverlappedResult(True)
BrokenPipeError: [WinError 109] The pipe has been ended
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\jerem\OneDrive\Projects\python\PokemonRedExperiments\baselines\run_baseline_parallel_fast.py", line 83, in <module>
model.learn(total_timesteps=(ep_length)*num_cpu*1000, callback=CallbackList(callbacks))
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\ppo\ppo.py", line 308, in learn
return super().learn(
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\common\on_policy_algorithm.py", line 259, in learn
continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\common\on_policy_algorithm.py", line 178, in collect_rollouts
new_obs, rewards, dones, infos = env.step(clipped_actions)
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\common\vec_env\base_vec_env.py", line 197, in step
return self.step_wait()
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\common\vec_env\vec_transpose.py", line 95, in step_wait
observations, rewards, dones, infos = self.venv.step_wait()
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\common\vec_env\subproc_vec_env.py", line 130, in step_wait
results = [remote.recv() for remote in self.remotes]
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\site-packages\stable_baselines3\common\vec_env\subproc_vec_env.py", line 130, in <listcomp>
results = [remote.recv() for remote in self.remotes]
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\multiprocessing\connection.py", line 250, in recv
buf = self._recv_bytes()
File "C:\Users\jerem\.conda\envs\PokemonRedExperiments\lib\multiprocessing\connection.py", line 321, in _recv_bytes
raise EOFError
EOFError
(PokemonRedExperiments) C:\Users\jerem\OneDrive\Projects\python\PokemonRedExperiments\baselines>
@dewmguy What you've pasted indicates the code had an issue writing out one of the jpeg files. It would be best if you could include information about the system you've executed the code on, steps to recreate the issue on-demand, etc.
pretty minimal. running in conda env in windows.