Application of reinforcement learning to a mobile robot in reaching recharging station operation

Efficient control strategies for robot systems cannot always be developed by hand, especially when the robot system is operating in an unknown or uncertain environment. In this paper we show how Reinforcement Learning (RL) might be applied to improve the efficiency of a mobile robot in nuclear decommissioning characterisation, in particular allowing it to learn efficient routes back to a recharging station. We implement this learning functionality in a mobile agent (MA) environment. By doing so we can make use of the positive characteristics of MA mobility such as adaptability, fault tolerance and dynamic positioning of learning or control in a distributed system to supplement learning. Experimental results show how RL provides a more efficient method in this task than a non-AI control approach.