Reinforcement Learning Control of a Real Mobile Robot Using Approximate Policy Iteration

Machine learning for mobile robots has attracted lots of research interests in recent years. However, there are still many challenges to apply learning techniques in real mobile robots, e.g., generalization in continuous spaces, learning efficiency and convergence, etc. In this paper, a reinforcement learning path-following control strategy based on approximate policy iteration (API) is developed for a real mobile robot. It has some advantages such as optimized control policies can be obtained without much a priori knowledge on dynamic models of mobile robot, etc. Two kinds of API-based control method, i.e., API with linear approximation and API with kernel machines, are implemented in the path following control task and the efficiency of the proposed control strategy is illustrated in the experimental studies on the real mobile robot based on the Pioneer3-AT platform. Experimental results verify that the API-based learning controller has better convergence and path following accuracy compared to conventional PD control methods. Finally, the learning control performance of the two API methods is also evaluated and compared.