schmidtdominik/Rainbow
Rainbow DQN implementation accompanying the paper "Fast and Data-Efficient Training of Rainbow" which reaches 205.7 median HNS after 10M frames. 🌈
This project helps machine learning researchers and reinforcement learning engineers train agents for game environments more efficiently. It takes raw game data from environments like Atari, Gym, or Procgen, processes it, and outputs a trained agent capable of playing these games. This is ideal for those focused on developing and evaluating advanced reinforcement learning algorithms with reduced computational demands.
No commits in the last 6 months.
Use this if you are a researcher or engineer looking to quickly train and benchmark Rainbow DQN agents on game environments with significantly less data and computational time.
Not ideal if you are a beginner looking for a simple, out-of-the-box solution for basic reinforcement learning tasks without prior experience in deep reinforcement learning.
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44
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4
Language
Python
License
MIT
Category
Last pushed
Dec 11, 2021
Commits (30d)
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