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ValueError: Env must be of one of the following supported types: BaseEnv, gym.Env, MultiAgentEnv, VectorEnv,
RemoteBaseEnv, ExternalMultiAgentEnv, ExternalEnv, but instead is of type class <'maze_gym_env.Environment'>.
The above error has been found in your environment! We've added a module for checking your custom environments. It may
cause your experiment to fail if your environment is not set up correctly. You can disable this behavior by setting
'disable_env_checking=True' in your environment config dictionary. You can run the environment checking module
standalone by calling ray.rllib.utils.check_env([env]).
I'm don't understand why I am getting this error as I followed your instructions to the letter. It's also not clear where the config dictionary is supposed to be stored.
I was wondering if you could help me resolve this issue?
Thank you!
The text was updated successfully, but these errors were encountered:
@kirmiziorman thanks so much for the feedback and spotting this mistake. I can't look into it today, but from the warning/error message it seems at least this can be turned off by setting disable_env_checking=True.
Likely the best way is to make it a gym.Env and sidestep this problem, which I thought we were already doing. Let me check this for you. We had a lot of moving parts in RLlib lately, so I apologise for the inconvenience here.
Hello @maxpumperla ,
Thank you for the amazing book, it's been a real pleasure working through it so far!
I am, however, having a problem with evaluating the maze game using RLlib in chapter 4. When I get to the command
rllib evaluate ~/ray_results/maze_env/ --algo DQN --env maze_gym_env.Environment --steps 100
I get the following error:
I'm don't understand why I am getting this error as I followed your instructions to the letter. It's also not clear where the config dictionary is supposed to be stored.
I was wondering if you could help me resolve this issue?
Thank you!
The text was updated successfully, but these errors were encountered: