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Project: Language Agents for Playing Card Games


The project is a pioneering initiative that combines Natural Language Processing (NLP) and Reinforcement Learning (RL) methodologies to create intelligent agents capable of understanding natural language instructions and participating in playing card games. This project aims to develop AI-driven agents that not only comprehend and generate language-based instructions but also exhibit strategic gameplay and decision-making abilities in the context of card games.

The key objectives of this joint NLP and RL project are outlined as follows:

Natural Language Understanding and Generation: Developing language models and algorithms that allow agents to comprehend and respond to natural language instructions related to playing card games. These models will enable the agents to interpret user commands, ask clarifying questions if needed, and generate coherent responses in natural language.

Reinforcement Learning for Gameplay Strategy: Implementing Reinforcement Learning techniques to train agents in the strategic aspects of playing card games. Agents will learn optimal gameplay strategies, decision-making processes, and adaptability based on game dynamics, rules, and opponent actions.

Interactive Gameplay Experience: Creating an interactive environment where users can communicate with the language-based agents using natural language instructions. The agents will participate in the gameplay, providing a seamless and immersive experience for players while understanding and responding to their commands.

Multi-agent Collaboration and Competition: Exploring how these language-driven agents interact and collaborate with each other in a multi-agent gaming environment. Additionally, investigating competitive scenarios where agents play against each other or human players, enhancing their adaptability and strategic capabilities.

Evaluation and Performance Metrics: Establishing metrics to evaluate the performance of the language-driven agents in playing card games. These metrics will assess the agents' language understanding, gameplay strategies, adaptability, and overall user experience.

This project aims to advance the state-of-the-art in both Natural Language Processing and Reinforcement Learning by developing agents that can understand and generate natural language in the context of card games while demonstrating sophisticated gameplay strategies and decision-making abilities. The outcomes of this joint endeavor could have far-reaching implications, influencing the development of AI systems not only in gaming but also in interactive conversational AI, personalized user experiences, and multi-agent collaborations in various domains.

Meng Fang
Secondary supervisor
Yudi Zhang
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