Concept Learning for Interpretable Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2302.12232v1
• Date: Thu, 23 Feb 2023 18:53:09 GMT
• Title: Concept Learning for Interpretable Multi-Agent Reinforcement Learning
• Authors: Renos Zabounidis, Joseph Campbell, Simon Stepputtis, Dana Hughes, Katia Sycara
• Abstract summary: We introduce a method for incorporating interpretable concepts from a domain expert into models trained through multi-agent reinforcement learning. This allows an expert to both reason about the resulting concept policy models in terms of these high-level concepts at run-time, as well as intervene and correct mispredictions to improve performance. We show that this yields improved interpretability and training stability, with benefits to policy performance and sample efficiency in a simulated and real-world cooperative-competitive multi-agent game.
• Score: 5.179808182296037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep neural network representations. We introduce a method for incorporating interpretable concepts from a domain expert into models trained through multi-agent reinforcement learning, by requiring the model to first predict such concepts then utilize them for decision making. This allows an expert to both reason about the resulting concept policy models in terms of these high-level concepts at run-time, as well as intervene and correct mispredictions to improve performance. We show that this yields improved interpretability and training stability, with benefits to policy performance and sample efficiency in a simulated and real-world cooperative-competitive multi-agent game.

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