Seth Karten will present his General Exam "Multi-Agent Frontiers with Foundation Models: Cooperative and Competitive Games" on May 6th, 2025 in Friend 125 at 12:30pm. Committee: Chi Jin (adviser), Benjamin Eysenbach, Danqi Chen Title: "Multi-Agent Frontiers with Foundation Models: Cooperative and Competitive Games" Abstract: Recent advances in foundation models, particularly large language models (LLMs), have created unprecedented opportunities to enhance multi-agent reinforcement learning (MARL) systems by leveraging their rich world knowledge and reasoning capabilities. This examination explores two complementary approaches to integrating LLMs with MARL across different game-theoretic settings. In zero-sum environments, we introduce PokéChamp, an expert-level minimax language agent for Pokémon battles that replaces three key components of traditional search algorithms with LLM-based modules: player action sampling, opponent modeling, and value function estimation. PokéChamp achieves human-expert performance without additional training, demonstrating the potential of LLMs to effectively constrain search spaces using informative priors in partially observable environments. For general-sum scenarios, we present the LLM Economist, a novel framework that employs LLMs to create realistic economic simulations with varied agent populations. By formulating the problem as a Stackelberg game, we show how LLMs can model complex economic behaviors through in-context optimization of utility functions, generation of synthetic human preferences, and mechanism design for social welfare maximization. These approaches collectively demonstrate a powerful synergy: MARL architectures benefit from the rich knowledge and reasoning capabilities embedded in foundation models, while reinforcement learning planning techniques provide the grounding and optimization mechanisms necessary to overcome the limitations of foundation models in strategic decision-making contexts.