Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Technical Update: Least-Squares Temporal Difference Learning
Machine Learning
Least-squares policy iteration
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Path-following control of mobile robots in presence of uncertainties
IEEE Transactions on Robotics
Kernel-Based Least Squares Policy Iteration for Reinforcement Learning
IEEE Transactions on Neural Networks
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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.