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Distributed Reinforcement Learning for Coordinate Multi-Robot Foraging
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DOI：10.1007/s10846-010-9429-4.png)
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In this paper, we propose a distributed dynamic correlation matrix based multi-Q (D-DCM-Multi-Q) learning method for multi-robot systems. First, a dynamic correlation matrix is proposed for multi-agent reinforcement learning, which not only considers each individual robot's Q-value, but also the correlated Q-values of neighboring robots. Then, the theoretical analysis of the system convergence for this D-DCM-Multi-Q method is provided. Various simulations for multi-robot foraging as well as a proof-of-concept experiment with a physical multi-robot system have been conducted to evaluate the proposed D-DCM-Multi-Q method. The extensive simulation/experimental results show the effectiveness, robustness, and stability of the proposed method.
Keyword:
Dynamic correlation matrix
Distributed multi-robot systems
Reinforcement learning
Multi-Q learning
Behavior-based reward
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