Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models
Keywords: LLM agents, language agents, theory of mind, multi-agent
TL;DR: We introduce Hypothetical Minds, an LLM-based agent that outperforms MARL and LLM baselines on multi-agent tasks using a novel Theory of Mind module
Abstract: Multi-agent reinforcement learning methods struggle with the nonstationarity of multi-agent systems and fail to learn online when tested with novel agents. Here, we leverage large language models (LLMs) to create an autonomous agent that can handle these challenges. Our agent, Hypothetical Minds, consists of a cognitively-inspired architecture, featuring modular components for perception, memory, and hierarchical planning over two levels of abstraction. We introduce the Theory of Mind module that scaffolds the high-level planning process by generating hypotheses about other agents' strategies in natural language. It then evaluates and iteratively refines these hypotheses by reinforcing hypotheses that make correct predictions about the other agents' behavior. Hypothetical Minds significantly improves performance over previous LLM-agent and RL baselines on a range of competitive, mixed motive, and collaborative domains in the Melting Pot benchmark, including both dyadic and population-based environments. Additionally, comparisons against LLM-agent baselines and ablations reveal the importance of hypothesis evaluation and refinement for succeeding on complex scenarios.
Submission Number: 101
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