Hierarchical Reinforcement Learning: A Survey <s> G. A Combined HRL Based Approach for Multi-robot Cooperative Target Searching in Complex Unknown Environments <s> Multi-agent reinforcement learning for multi-robot systems is a challenging issue in both robotics and artificial intelligence. But multi-agent reinforcement learning is bedeviled by the curse of dimensionality. In this paper, a novel hierarchical reinforcement learning approach named MOMQ is presented for multi-robot cooperation. The performance of MOMQ is demonstrated in three-robot trash collection task. <s> BIB001 </s> Hierarchical Reinforcement Learning: A Survey <s> G. A Combined HRL Based Approach for Multi-robot Cooperative Target Searching in Complex Unknown Environments <s> Effective cooperation of multi-robots in unknown environments is essential in many robotic applications, such as environment exploration and target searching. In this paper, a combined hierarchical reinforcement learning approach, together with a designed cooperation strategy, is proposed for the real-time cooperation of multi-robots in completely unknown environments. Unlike other algorithms that need an explicit environment model or select parameters by trial and error, the proposed cooperation method obtains all the required parameters automatically through learning. By integrating segmental options with the traditional MAXQ algorithm, the cooperation hierarchy is built. In new tasks, the designed cooperation method can control the multi-robot system to complete the task effectively. The simulation results demonstrate that the proposed scheme is able to effectively and efficiently lead a team of robots to cooperatively accomplish target searching tasks in completely unknown environments. <s> BIB002
Collaboration of multi-robots in unknown environments is one of the fundamentals in various applications like target searching and exploring the environments. One of the main weaknesses is that the learning ability in many RL approaches is temporary due to the reason that it is environment-based; the ability to deal with new environments and specifically dynamic environments. BIB001 suggested a combination of both Option and MAXQ algorithms in which the knowledge and the hierarchical structure are introduced and constructed respectively by both algorithms. However, this solution still lacks the exploration of the unnecessary parts of the environments. BIB002 proposed an effective HRL algorithm that combines both the MAXQ and Option algorithms (as in Figure 3 ) where all the required parameters will be automatically obtained through the learning, unlike other algorithms that selects parameters via trial and error. The proposed solution has the ability to evaluate the feedback and tries to get featured parameters for future processes; the reason that makes this solution is unique for such environments as comparing with the others. The simulation results revealed that the proposed solution has the ability to allow a team of robots to collaboratively achieve target searching in unknown environments. http://journals.uob.edu.bh 