PokerRL

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PokerRL is an Open Source research & education tool that provides a framework for multi-agent deep reinforcement learning in poker games. It supports the development and evaluation of AI agents for strategic game playing.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is PokerRL?

PokerRL is an open-source framework designed for multi-agent deep reinforcement learning in poker games. It facilitates research into solving imperfect information games by combining Deep Reinforcement Learning with conventional methods like CFR and Fictitious-Play to learn approximate Nash equilibria. The framework includes components for building algorithms with interacting workers, evaluating performance using metrics like Best Response and Head-To-Head, and offers scalability through integration with Ray for local, multi-core, or cluster deployments. It supports both 2-player and N-player games, providing a robust environment for developing and testing AI agents in complex strategic scenarios.

Best used for

Ideal for professors who need to develop and evaluate AI agents for poker games, research imperfect information games, and test reinforcement learning algorithms. Especially valuable for those requiring distributed computing capabilities for large-scale experiments.

Common actions

develop AI agents

research reinforcement learning

evaluate game AI

simulate poker games

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Capabilities

Key features

Multi-agent deep reinforcement learning
Poker game engine
Algorithm evaluation metrics
Distributed computing support
Local/cluster deployment
Approximate Nash equilibria learning

Target Audience

professor

Integrations

raytensorboard

Pricing & Plans

Open Source

Free

FAQs

What kind of poker games does PokerRL support?

PokerRL's game engine and agent modules are general to N>1 players, meaning they can support various poker game formats. However, some parts of the framework are specifically designed for 2-player games, indicating a primary focus on heads-up scenarios.

How does PokerRL evaluate the performance of AI algorithms?

PokerRL offers four main metrics for algorithm evaluation: Best Response (BR) for exact exploitability in small games, Local Best Response (LBR) and RL Best Response (RL-BR) for approximations in larger games, and Head-To-Head (H2H) performance between agents.

Can PokerRL be deployed on a cloud cluster?

Yes, PokerRL provides an interface that allows the same code to run locally or on a cluster by utilizing Ray. It supports both distributed mode on a single machine with many cores and cluster mode across multiple machines, including AWS instances.

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