A Hierarchical Reinforcement Learning Based Approach for Multi-robot Cooperation in Unknown Environments
Reinforcement learning is a good method for multi-robot systems to handle tasks in unknown environments or with obscure models. MAXQ is a hierarchical reinforcement learning algorithm, which is limited by some inherent problems. In addition, much research has focused on the completion of the task, rather than the ability to deal with new tasks. In this paper, an improved MAXQ approach is adopted to tune the parameters of the cooperation rules. The proposed scheme is applied to target searching tasks by multi-robots. The simulation results demonstrate the effectiveness and efficiency of the proposed scheme.
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