DocumentCode :
3424821
Title :
Adaptive action selection using utility-based reinforcement learning
Author :
Chen, Kunrong ; Lin, Fen ; Tan, Qing ; Shi, Zhongzhi
Author_Institution :
Key Lab. of Intell. Inf. Process., Chinese Acad. of Sci., Beijing, China
fYear :
2009
fDate :
17-19 Aug. 2009
Firstpage :
67
Lastpage :
72
Abstract :
A basic problem of intelligent systems is choosing adaptive action to perform in a non-stationary environment. Due to the combinatorial complexity of actions, agent cannot possibly consider every option available to it at every instant in time. It needs to find good policies that dictate optimum actions to perform in each situation. This paper proposes an algorithm, called UQ-learning, to better solve action selection problem by using reinforcement learning and utility function. Reinforcement learning can provide the information of environment and utility function is used to balance exploration-exploitation dilemma. We implement our method with maze navigation tasks in a non-stationary environment. The results of simulated experiments show that utility-based reinforcement learning approach is more effective and efficient compared with Q-learning and recency-based exploration.
Keywords :
Markov processes; combinatorial mathematics; computational complexity; decision theory; learning (artificial intelligence); multi-agent systems; Markov decision process; UQ-learning algorithm; adaptive action selection; balance exploration-exploitation dilemma; combinatorial complexity; intelligent system; maze navigation task; multiagent system; nonstationary environment; recency-based exploration; utility function; utility-based reinforcement learning algorithm; Adaptive systems; Computers; Environmental management; Information processing; Intelligent agent; Intelligent systems; Learning; Management training; Navigation; Simulated annealing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2009, GRC '09. IEEE International Conference on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-4830-2
Type :
conf
DOI :
10.1109/GRC.2009.5255163
Filename :
5255163
Link To Document :
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