Kaixhin/Atari
Persistent advantage learning dueling double DQN for the Arcade Learning Environment
This project helps researchers and practitioners in artificial intelligence train and evaluate reinforcement learning agents on classic Atari games or custom simulation environments. You provide game ROMs or a defined environment, and it outputs a trained agent that can play the game, along with performance metrics and visual saliency maps. This tool is designed for AI researchers, machine learning engineers, and data scientists working on advanced reinforcement learning algorithms.
263 stars. No commits in the last 6 months.
Use this if you are developing or testing new deep reinforcement learning algorithms and need a robust framework to train agents on video game environments.
Not ideal if you are a casual gamer looking for an AI opponent or if you need a simple, off-the-shelf solution for game automation.
Stars
263
Forks
72
Language
Lua
License
MIT
Category
Last pushed
Feb 08, 2018
Commits (30d)
0
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