I’m having trouble setting up an Atari game environment and getting a ROM-related error.
When I try to create a game environment using the code below, it throws an error about missing game ROMs:
import gym
game_env = gym.make('Pong-v4')
Error message I’m getting:
/usr/local/lib/python3.8/site-packages/ale_py/gym/environment.py:11: DeprecationWarning: Importing atari-py roms won't be supported in future releases of ale-py.
import ale_py.roms as roms
A.L.E: Arcade Learning Environment (version +b65c452)
[Powered by Stella]
Traceback (most recent call last):
File "test_gym.py", line 2, in <module>
game_env = gym.make('Pong-v4')
File "/usr/local/lib/python3.8/site-packages/gym/envs/registration.py", line 200, in make
return registry.make(id, **kwargs)
File "/usr/local/lib/python3.8/site-packages/gym/envs/registration.py", line 105, in make
env = spec.make(**kwargs)
File "/usr/local/lib/python3.8/site-packages/ale_py/gym/environment.py", line 123, in __init__
self.seed()
File "/usr/local/lib/python3.8/site-packages/ale_py/gym/environment.py", line 171, in seed
raise error.Error(
gym.error.Error: Unable to find game "Pong", did you import Pong with ale-import-roms?
The error suggests using ale-import-roms but I’m not sure how to fix this properly. Has anyone encountered this before?
ROM issues? That’s nothing compared to what’s coming. Yeah, pip install gym[atari,accept-rom-license] fixes it, or just import ROMs manually.
But nobody warns you about the real headaches - training multiple agents, juggling experiment configs, model versioning, monitoring, deployment. It gets messy fast.
I wasted weeks manually running RL pipelines. Started simple with gym environments, then boom - suddenly I’m scheduling training runs, triggering retraining when performance tanks, syncing results everywhere.
That’s when automation becomes your lifeline. Latenode builds these complex RL workflows without writing infrastructure code from scratch. Hook up training scripts to databases, automate hyperparameter sweeps, deploy models based on validation scores.
Fix your ROM problem now, but plan ahead. When you’re ready to scale past local experiments, automation saves months of grunt work.
You’re hitting this because gym doesn’t include Atari ROMs anymore due to copyright issues. Same thing happened to me when I started with Atari environments. Easiest fix is grabbing the ROM pack from the Atari 2600 VCS ROM Collection, then using ale-import-roms to import them. You can find legal ROM downloads from various sources online. After downloading the ROMs to a folder, run: bash ale-import-roms /path/to/your/rom/folder If you want to test things quickly, try one of the newer ALE environments with different ROM requirements, or switch to a non-Atari environment while you figure out the ROM situation. Once you get the ROMs imported properly, environment creation works perfectly. That deprecation warning is just informational - won’t break your code.
Had this exact issue migrating an older project to newer gym versions. The ROM licensing change caught me off guard too. Found a cleaner approach that works reliably instead of hunting down ROMs. First, completely uninstall your current atari setup: bash pip uninstall gym atari-py ale-py Then reinstall with the right dependencies: bash pip install gym[atari,accept-rom-license] The accept-rom-license flag automatically handles ROM installation - no manual imports needed. Works consistently across different systems. Still getting ROM errors? Try the newer gymnasium package instead of gym. It’s got better Atari environment support. API’s nearly identical but handles these dependency issues way more gracefully.
This happens because OpenAI Gym doesn’t include Atari ROMs due to licensing issues. You’ll need to install them separately.
Quick fix:
pip install gym[atari]
ale-import-roms
But if you’re doing serious RL work, you’ll face much bigger automation problems later. Training runs, hyperparameter sweeps, model deployments, data pipelines - it gets messy fast.
I’ve been there. Started with simple gym environments and quickly needed to orchestrate complex ML workflows across multiple tools and APIs. That’s when automation platforms become essential.
Latenode handles this workflow automation really well. You can automate training pipelines, connect RL models to databases, trigger retraining based on performance metrics, and deploy models to production automatically.
The ROM fix above works for now. But when you need to scale past local experiments, Latenode will save you weeks of infrastructure headaches.
If the ROM stuff keeps giving you headaches, try switching to ALE-Py directly instead of going through gym. Install with pip install ale-py[gym] then use gym.make('ALE/Pong-v5') instead of the old v4 naming. The v5 environments handle ROM loading differently and usually work better out of the box.