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RLCard is a toolkit for reinforcement learning research on card games. It includes several popular card games and focuses on learning algorithms for imperfect information games like poker and blackjack.
Features
- Provides a suite of card game environments for RL training
- Includes implementations of Texas Hold’em, Blackjack, and others
- Focuses on imperfect information and multi-agent learning
- Compatible with Gym-like APIs for easy integration
- Supports opponent modeling and strategy learning
- Includes benchmark models for performance comparison
Project Samples
Categories
Reinforcement Learning LibrariesLicense
MIT LicenseFollow RLCard
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