Using Prior Knowledge to Improve Reinforcement Learning in Mobile Robotics

Reinforcement learning (RL) is thought to be an appropriate paradigm for acquiring control policies in mobile robotics. However, in its standard formulation (tabula rasa) RL must explore and learn everything from scratch, which is neither realistic nor effective in real-world tasks. In this article we propose a new strategy, called Supervised Reinforcement Learning (SRL), for taking advantage of external knowledge within this type of learning and validate it in a wall-following behaviour.

[1]  C. Watkins.  Learning from delayed rewards , 1989 .

[2]  John S. Bridle,et al.  Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters , 1989, NIPS.

[3]  Paul E. Utgoff,et al.  A Teaching Method for Reinforcement Learning , 1992, ML.

[4]  Michael L. Littman,et al.  Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach , 1993, NIPS.

[5]  Gerald Tesauro,et al.  TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play , 1994, Neural Computation.

[6]  Maja J. Mataric,et al.  Reward Functions for Accelerated Learning , 1994, ICML.

[7]  S. Schaal,et al.  Robot juggling: implementation of memory-based learning , 1994, IEEE Control Systems.

[8]  Jeremy L. Wyatt.  Issues in Putting Reinforcement Learning Onto Robots , 1995 .

[9]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[10]  Senén Barro,et al.  Supervised Reinforcement Learning: Application to a Wall Following Behaviour in a Mobile Robot , 1998, IEA/AIE.

[11]  K. R. Dixon,et al.  Incorporating Prior Knowledge and Previously Learned Information into Reinforcement Learning Agents , 2000 .

[12]  Getachew Hailu,et al.  Symbolic structures in numeric reinforcement for learning optimum robot trajectory , 2001, Robotics Auton. Syst..

[13]  Senén Barro,et al.  A Control Architecture for Mobile Robotics Based on Specialists , 2002 .

[14]  José del R. Millán,et al.  Continuous-Action Q-Learning , 2002, Machine Learning.

[15]  Longxin Lin.  Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching , 2004, Machine Learning.

[16]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.