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In this paper, we propose a Q-learning with continuous action policy and extend this algorithm to a multi-agent system. We examine this algorithm in a task that there are two robots taking action independently but connected with a straight bar. The robots must cooperate to move to the goal and avoid the obstacles in the environment. Conventional Q-learning needs a pre-defined and discrete state space...
To design appropriate actions of mobile robots, the designers usually observe the sensory signals on the robots and decide the actions from the viewpoint of some desired purposes. This approach needs deliberative consideration and abundant knowledge on robotics for a variety of situations. To improve the actions of robots, it is hard to sense the error by human eyes and takes time in trial-and-error...
This research focuses on how a multi-robot system can work cooperatively to complete patrol missions. The planning and assignment of patrol points to the robots are the major issues to tackle with. A cooperative auction system (CAS) is proposed to solve the problem of patrol planning. Here, each mobile robot picks its own patrol points via the cooperative auction system and the system will continuously...
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