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- W4246917522 abstract "This chapter introduces a novel approach to adapt composite rewards of all the agents in one Q-table in joint state-action space during learning, and uses these rewards to compute correlated equilibrium in the planning phase. The ΩQ-learning algorithms proposed in this chapter have two attractive features, which are not available in the traditional correlated Q-learning (CQL). First, during the learning phase, an agent needs to adapt only one Q-table in joint state—action space unlike adapting m joint Q-tables for m agents in CQL. Second, the evaluation of the computationally expensive correlated equilibrium is avoided, following a tricky approach of computing it partially during the learning and the rest during the planning phases. The chapter provides an overview of the single agent Q-learning and equilibrium-based multi-agent Q-learning (MAQL) algorithms. Proposed cooperative MAQL and corresponding planning algorithms are then provided. The chapter offers complexity analysis, and presents simulation and experimental results." @default.
- W4246917522 created "2022-05-12" @default.
- W4246917522 date "2020-11-06" @default.
- W4246917522 modified "2023-10-16" @default.
- W4246917522 title "An Efficient Computing of Correlated Equilibrium for Cooperative Q‐Learning‐Based Multi‐Robot Planning" @default.
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- W4246917522 doi "https://doi.org/10.1002/9781119699057.ch4" @default.
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