Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Apply an algorithm to an Atari game #7

Closed
schrum2 opened this issue May 22, 2019 · 5 comments
Closed

Apply an algorithm to an Atari game #7

schrum2 opened this issue May 22, 2019 · 5 comments
Assignees

Comments

@schrum2
Copy link
Collaborator

schrum2 commented May 22, 2019

Not sure how much work is required for this. Open AI Gym supports interfacing to Atari games, but I think you still have to compile ALE (Arcade Learning Environment) with the Stella Atari emulator to take advantage. You may also need to have the appropriate ROM files.

Still, investigate the use of Open AI Gym for Atari games and see if you can apply a DQN to one.

@schrum2
Copy link
Collaborator Author

schrum2 commented May 28, 2019

From what I recall, the gym-based Atari options are Linux/Mac only. Can you update this thread with some of the details on this?

What about Docker and/or VirtualBox based work-arounds for this?

@schrum2
Copy link
Collaborator Author

schrum2 commented May 30, 2019

Atari might be possible. Look at this thread:
openai/gym#11
Specifically, skip down to the post by icoxfog417

@schrum2
Copy link
Collaborator Author

schrum2 commented May 30, 2019

This also looks promising:
https://github.com/rybskej/atari-py

@schrum2
Copy link
Collaborator Author

schrum2 commented Jun 5, 2019

Thanks to gym-retro, we have also had success with Atari games. Therefore, we are close to closing this issue. This is what is left to do:

  1. Get DQN to work on Atari games. Either modify our copy of DQN or get the DQN baseline implementation from Open AI's baseline repo: https://github.com/openai/baselines
  2. Train a DQN on at least two of the games from this paper: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
  3. Log information in this GitHub thread about how long it takes to train, and what level of performance we attain on each game you test.

Then close the issue.

@schrum2
Copy link
Collaborator Author

schrum2 commented Jul 9, 2019

No more time for benchmarks, though we seem to be able to learn in Atari games

@schrum2 schrum2 closed this as completed Jul 9, 2019
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants