Abstract
New representation and computation mechanisms are key approaches for learning problems with incomplete information or in large probabilistic environments. In this paper, traditional reinforcement learning (RL) methods are combined with grey theory and a novel grey reinforcement learning (GRL) framework is proposed to solve complex problems with incomplete information. Typical example of mobile robot navigation is given out to evaluate the performance and practicability of GRL. Related issues are also briefly discussed.
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References
Sutton, R., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research 4, 237–287 (1996)
Sutton, R.: Learning to Predict by the Methods of Temporal Difference. Machine Learning 3, 9–44 (1988)
Bertsekas, D. P., Tsitsiklis, J. N.: Neuro-Dynamic Programming. Athena Scientific, Belmont, MA(1996)
Watkins, C., Dayan, P.: Q-learning. Machine Learning 8, 279–292 (1992)
Santamaría, J., Sutton, R., Ram, A.: Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces. Adaptive Behavior 6, 163–217 (1997)
Smart, W.D., Kaelbling, L.P.: Effective reinforcement learning for mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3404–3410. IEEE Press, Los Alamitos (2002)
Kondo, T., Ito, K.: A reinforcement learning with evolutionary state recruitment strategy for autonomous mobile robots control. Robotics and Autonomous Systems 46, 111–124 (2004)
Beom, H.R., Cho, H.S.: A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning. IEEE Transactions on Systems Man and Cybernetics 25, 464–477 (1995)
Wiering, M., Schmidhuber, J.: HQ-Learning. Adaptive Behavior 6, 219–246 (1997)
Barto, A.G., Mahanevan, S.: RecentAdvances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems: Theory and Applications 13, 41–77 (2003)
Sutton, R., Precup, D., Singh, S.: Between Mdps and Semi-mdps: A Framework for Temporal Abstraction in Reinforcement Learning. Artificial Intelligence 112, 181–211 (1999)
Parr, R., Russell, S.: Reinforcement learning with hierarchies of machines. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 1043–1049. MIT Press, Cambridge (1998)
Dietterich, T.G.: Hierarchical Reinforcement Learning with the Maxq Value Function Decomposition. Journal of Artificial Intelligence Research 13, 227–303 (2000)
Theocharous, G.: Hierarchical Learning and Planning in Partially Observable Markov Decision Processes. Department of Computer Science and Engineering, Michigan State University, USA (2002)
Dong, D.Y., Chen, C.L., Chen, Z.H.: Quantum reinforcement learning. In: Natural Computation-ICNC2005. LNCS, vol. 3611, pp. 686–689. Springer, Heidelberg (2005)
Smith, A.J.: Applications of the self-organising map to reinforcement learning. Neural Networks 15, 1107–1124 (2002)
Glorennec, P.Y., Jouffe, L.: Fuzzy Q-learning. In: Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, pp. 659–662. IEEE Press, Los Alamitos (1997)
Dong, D.Y., Chen, C.L., Zhang, C.B., Chen, Z.H.: An Autonomous Mobile Robot Based on Quantum Algorithm. In: CIS 2005. LNCS (LNAI), vol. 3801, pp. 394–399. Springer, Heidelberg (2005)
Likas, A.: Reinforcement learning using the stochastic fuzzy min-max neural network. Neural Processing Letters 13, 213–220 (2001)
Deng, J. L.: Elements on Grey Theory. HUST Press, Wuhan, China (2002) (in Chinese)
Kuipers, B.: Qualitative simulation. Artificial Intelligence, 289–338 (1986)
Berleant, D., Kuipers, B.: Qualitative and quantitative simulation: bridging the gap. Artificial Intelligence 95, 215–255 (1997)
Huang, Y.L.: Research on Grey Qualitative Simulation Base. University of Science and Technology of China (2004) (in Chinese)
Huang, Y.L., Chen, Z.H., Gui, W.S.: Grey Qualitative Simulation. The Journal of Grey System 1, 5–20 (2004)
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Chen, C., Dong, D., Chen, Z. (2006). Grey Reinforcement Learning for Incomplete Information Processing. In: Cai, JY., Cooper, S.B., Li, A. (eds) Theory and Applications of Models of Computation. TAMC 2006. Lecture Notes in Computer Science, vol 3959. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11750321_38
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DOI: https://doi.org/10.1007/11750321_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34021-8
Online ISBN: 978-3-540-34022-5
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