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Walking through narrow space for multi-legged robot is optimized using reinforcement learning in this paper. The walking is generated by the virtual repulsive force from the estimated obstacle position and the virtual impedance field. The resulted action depends on the parameter of the virtual impedance coefficients. The reinforcement learning is employed to find an optimal motion. The temporal walking...
This paper proposes new reinforcement learning approaches for dispersion games in multi-agent systems. Multi-agent systems can establish orderly systems autonomously through interaction with autonomous agents. We expect to be able to build flexible and robust systems for the environmental changes by using multi-agent system approaches. However, it is difficult for designers to preliminarily embed...
Reinforcement learning agents store their knowledge such as state-action value in look-up tables. However, loop-up table requires large memory space when number of states become large. Learning from look-up table is tabularasa therefore is very slow. To overcome this disadvantage, generalization methods are used to abstract knowledge. In this paper, decision tree technology is used to enable the agent...
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