Mobile Robot Navigation: Neural Q-Learning
This paper presents the mobile robot navigation technique which utilizes Reinforcement Learning (RL) algorithms and Artificial Neural Network (ANN) to learn in an unknown environment for mobile robot navigation. This process is divided into two stages. In the initial stage, the agent will map the environment through collecting state-action information according to the Q-Learning procedure. Second training process involves Neural Network, which utilizes the state-action information gathered in the earlier phase of training samples. During final application of the controller, Q-Learning would be used as primary navigating tool whereas the trained Neural Network will be employed when approximation is needed. MATLAB simulation was developed to verify and validate the algorithm before real time implementation using Team AmigoBotTM robot. The results obtained from both simulation and real world experiments are discussed.
KeywordsReinforcement Learning (RL) Q-Learning Artificial Neural Network (ANN) Neural Q-Learning Team AmigoBotTM MATLAB
Unable to display preview. Download preview PDF.
- 1.Watkins, C. J.C.H., Dayan, P.: Q-Learning. In: Machine Learning, vol. 8, pp. 279–292 (1992)Google Scholar
- 2.Sutton, R.S., Barto, A.G.: Reinforcement Learning: an Introduction. MIT Press, Cambridge (1998)Google Scholar
- 6.Li, C., Zhang, J., Li, Y.: Application of Artificial Neural Network Based on Q-Learning for Mobile Robot Path Planning. In: Proceedings of 2006 IEEE International Conference on Information Acquisition, Shandong, People’s Republic of China, pp. 978–982 (2006)Google Scholar
- 7.Pyeatt, L.D., Howe, A.E.: Decision Tree Function Approximation in Reinforcement Learning. In: Proceedings of the Third International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Models, Havana, Cuba (1998)Google Scholar
- 8.Smart, W.D., Kiebling, L. P.: Effective Reinforcement Learning for Mobile Robots. In: Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, DC, USA, pp. 3404–3410 (2002)Google Scholar
- 9.Kondo, T., Ito, K.: A Reinforcement Learning with Evolutionary State Recruitment Strategy for Autonomous Mobile Robots Control. Robotics and Autonomous Systems 46(2), 111–124 (2004)Google Scholar