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By Jiming Liu
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Their scheme consists of four layers of modular control, corresponding to four stages of reinforcement learning that start with easier problems and proceed to more complicated ones. It should be mentioned that the amount and quality of reinforcement can determine how quickly an agent will learn. In a nondeterministic, uncertain world, learning within bounded time requires reinforcement shaping in order to take advantage of as much information as available to the agent. ” In the case of a multi-agent system, Yamaguchi et al.
Another way is through the feedback of the information that is observed globally from the whole system, such as the spatial pattern formation in group robots. For example, a particular group of robots switches from one behavior to another as commanded by a global control mechanism; or the behavior parameters in a particular group change in a certain way. From the above descriptions, we now come to the following questions: in order to achieve an optimal multi-robot performance, how much optimization at the local individual level and how much at the global level will be necessary?
BTC98, BT95] achieved this by defining specific communication mechanisms. Generally speaking, the characteristics of self-organization are desirable in a swarm of robots where simple individual behaviors can be achieved with relatively low cost [Bon98]. At the same time, the simplicity (and homogeneity) of individual agents in a robotic swarm also decreases the likelihood of breakdown. Also, the breakdown of one agent will not affect the activity of the whole robotic swarm [Ste95]. 2 Local vs.