nadavbh12/Retro-Learning-Environment
The Retro Learning Environment (RLE) -- a learning framework for AI
Provides multi-console game environments (Atari, SNES) for RL training by wrapping Libretro emulators (Stella, SNES9X) with raw screen pixel input as the learning signal. Offers Python, C++, and Lua/Torch interfaces, integrating with OpenAI Gym for algorithm evaluation across 30+ classic titles. **Note: Project is deprecated in favor of OpenAI Gym-Retro**, which supersedes its feature set and console support.
186 stars. No commits in the last 6 months.
Stars
186
Forks
41
Language
C++
License
—
Category
Last pushed
Jun 06, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/nadavbh12/Retro-Learning-Environment"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Farama-Foundation/stable-retro
Retro games for Reinforcement Learning Research
MatPoliquin/stable-retro-scripts
Train models on retro games. AI vs AI contest. Pytorch C++ plugin for RetroArch that let you...
Avalon-Benchmark/avalon
A 3D video game environment and benchmark designed from scratch for reinforcement learning research
svpino/cs7641-assignment4
CS7641 - Machine Learning - Assignment 4 - Markov Decision Processes
abhinavcreed13/ai-reinforcement-learning
This project will implement value iteration and Q-learning. It will first test agents on...