论文信息 - Achieving Coverage through Distributed Reinforcement Learning in Wireless Sensor Networks

Achieving Coverage through Distributed Reinforcement Learning in Wireless Sensor Networks

With the extensive implementations of wireless sensor networks in many areas, it is imperative to have better management of the coverage and energy consumption of such networks. These networks consist of large number of sensor nodes and therefore a multi-agent system approach needs to be taken in order for a more accurate model. Three coordination algorithms are being put to the test in this paper: (i) fully distributed Q-learning which we refer to as independent learner (IL), (ii) distributed value function (DVF) and (iii) an algorithm we developed which is a variation of the IL, coordinated algorithm (COORD). The results show that the IL and DVF algorithm performed for higher sensor node densities but at low sensor node densities, the three algorithms have similar performance.

[1] Xiaofeng Wang,et al. Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games , 2002, NIPS.

[2] Andrew W. Moore,et al. Distributed Value Functions , 1999, ICML.

[3] Michael L. Littman,et al. Friend-or-Foe Q-learning in General-Sum Games , 2001, ICML.

[4] Ben J. A. Kröse,et al. Learning from delayed rewards , 1995, Robotics Auton. Syst..

[5] Michael L. Littman,et al. Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.

[6] Michael P. Wellman,et al. Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm , 1998, ICML.

[7] Krishnendu Chakrabarty,et al. Sensor placement for effective coverage and surveillance in distributed sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[8] Craig Boutilier,et al. The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems , 1998, AAAI/IAAI.

[9] Peter Dayan,et al. Q-learning , 1992, Machine Learning.

[10] Chen-Khong Tham,et al. Coordinated Sensing Coverage in Sensor Networks using Distributed Reinforcement Learning , 2006, 2006 14th IEEE International Conference on Networks.