Arcade-Learning-Environment

Atari games are widely used in Reinforcement Learning (RL) research as a standard benchmark. https://github.com/Farama-Foundation/Arcade-Learning-Environ... The goal is to develop algorithms that generalize to other tasks.

Thanks for that! So I was looking at the one for Montezuma's Revenge: https://github.com/mgbellemare/Arcade-Learning-Environment/b... The reward seems to be only the game score which I believe is meant to be problematic for this game because your score doesn't go up very often (so you have to perform a lot of actions to get any feedback)? The lives are recorded but aren't part of the "get reward" method...are the lives factored into decision making somewhere else? Seems like knowing you just lost a life would really help decision making for such a game.

Yes. For example, Breakout saves score in address 76 and 77. Arcade Learning Environment has code to read the score, one per game. Code for Breakout is here: https://github.com/mgbellemare/Arcade-Learning-Environment/b...

Other tools for developing / comparing RL algorithms: * Burlap (from Brown-UMBC) https://github.com/jmacglashan/burlap * RL Glue http://glue.rl-community.org/wiki/Main_Page Also looks like some of the challenges come from ALE: https://github.com/mgbellemare/Arcade-Learning-Environment