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Interfaces for human players to compete against pre-trained or rule-based AI models through terminal or GUI interaction.
Distinct from Pre-trained Model Application: Distinct from Pre-trained Model Application: focuses on interactive human-vs-AI play, not general downstream task deployment.
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RLcard is an open-source framework for developing and evaluating reinforcement learning agents across multiple card game environments. It functions as a card game environment simulator, a multi-agent RL platform, and a benchmarking toolkit for algorithms like DQN, NFSP, and CFR. The framework provides a game-agnostic environment interface that decouples agent logic from game mechanics, allowing any policy to interact through a common API. It supports pluggable reinforcement learning algorithms that operate on this interface without modifying game logic, and includes a self-play training loop
Loads a trained or rule-based agent and takes actions interactively through a terminal or GUI.