Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement
Learning
- URL: http://arxiv.org/abs/2307.01158v2
- Date: Tue, 18 Jul 2023 18:04:08 GMT
- Title: Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement
Learning
- Authors: Ini Oguntola, Joseph Campbell, Simon Stepputtis, Katia Sycara
- Abstract summary: We present a method of grounding semantically meaningful, human-interpretable beliefs within policies modeled by deep networks.
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.
- Score: 5.314466196448188
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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