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Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning
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The ability to model the mental states of others is crucial to human social intelligence, and can offer similar benefits to artificial agents with respect to the social dynamics induced in multi-agent settings. We present a method of grounding semantically meaningful, human-interpretable beliefs within policies modeled by deep networks. We then consider the task of 2nd-order belief prediction. We propose that ability of each agent to predict the beliefs of the other agents can be used as an intrinsic reward signal for multi-agent reinforcement learning. Finally, we present preliminary empirical results in a mixed cooperative-competitive environment.
Forward citations
Cited by 4 Pith papers
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Attention heads in multimodal LLMs linearly encode agents' beliefs, and steering those heads along probe-derived directions improves first- and second-order belief accuracy on the new GridToM benchmark.
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Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details
For Other-Play in Yokai, agents trained with different implementation details coordinate across implementations about as well as across seeds, supporting inter-seed cross-play as a proxy for cross-implementation evaluation.
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Towards Machine Theory of Mind with Large Language Model-Augmented Inverse Planning
An LLM-augmented Bayesian inverse planning model, LAIP, generates hypotheses and action likelihoods, then uses Bayes' rule to infer agent preferences, outperforming LLM-only baselines.
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