EricSteinberger/PokerRL
Framework for Multi-Agent Deep Reinforcement Learning in Poker
This project provides a toolkit for researchers and AI developers focused on creating and evaluating intelligent agents for multi-player poker games. It allows you to design and train AI poker players using advanced deep reinforcement learning techniques. You provide the algorithm logic, and it outputs a trained AI agent capable of playing poker against other AIs or humans, along with performance metrics.
512 stars. No commits in the last 6 months.
Use this if you are developing new AI algorithms for imperfect information games like poker and need a scalable framework to train and evaluate your agents.
Not ideal if you are simply looking for an out-of-the-box poker bot to play with, or if you are not experienced with deep reinforcement learning and AI development.
Stars
512
Forks
106
Language
Python
License
MIT
Category
Last pushed
Mar 31, 2023
Commits (30d)
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