Learning the Point Gathering Task Using Shared Value Function In Mobile Robots
22 Jan 2009-pp 9-13
TL;DR: The algorithm was analyzed through simulations for up to 5 robots and the analysis indicates that communication helped robots perform significantly faster than when they acted independently - measured using the path-length of the first robot to reach the goal as the metric.
Abstract: Managing the actions of several agents to perform tasks which require coordination and cooperation pose significant research challenges. One such challenge is to synchronize the agents’ view of the system to help them take the ‘right’ actions. In this paper, we propose an algorithm called MRCPG (Mobile Robot Coordination Point Gathering Algorithm) for coordinating the actions of a team of mobile robots. The aim is to gather these robots at a particular location in a 2-dimensional plane which is determined during execution. The robots are randomly deployed in the plane and they achieve the goal by communicating periodically. In addition, we impose a Reinforcement Learning framework and the robots learn a Shared Value Function (SVF) based on scalar rewards received. The SVF is used to select the best possible action in each state until at least one robot reaches the goal. Then a Reach-distance heuristic is used to direct the remaining robots to the goal. The algorithm was analyzed through simulations for up to 5 robots and the analysis indicates that communication helped robots perform significantly faster than when they acted independently - measured using the path-length of the first robot to reach the goal as the metric. We also observed that increasing team size enhances the effect of communication and hastens task completion.
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17 Dec 2015
TL;DR: This work presents a novel approach to solving the distribution of the environmental feedback signal among learning agents in Multiagent Reinforcement Learning (MARL).
Abstract: In Multiagent Reinforcement Learning (MARL), a single scalar reinforcement signal is the sole reliable feedback that members of a team of learning agents can receive from the environment around them. Hence, the distribution of the environmental feedback signal among learning agents, also known as the “Multiagent Credit Assignment” (MCA), is among the most challenging problems in MARL.
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01 Jan 2006
TL;DR: A group of mobile autonomous robots, each with very limited capabilities, can form (complex) patterns in the space it occupies, and researchers are studying what patterns can be formed and how.
Abstract: A group of mobile autonomous robots, each with very limited capabilities, can form (complex) patterns in the space it occupies. These patterns can be used to program the robots to accomplish high-level tasks (e.g., surrounding and removal of a mine). The basic research questions address which patterns can be formed, and how they can be formed. These questions have been studied mostly from an empirical point of view. Most solutions do not have any guarantee of correctness; actually many solutions never terminate and never form the desired pattern. On the contrary, we are interested in (provably correct) solutions which always form the pattern within finite time. With this goal, we have been studying what patterns can be formed and how; in this paper we describe the results of our investigations.
6 citations