Abstract
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agent is faced with the problem of assessing the desirability of the state it finds itself in. If the state space is very large and/or continuous the availability of a suitable mechanism to approximate a value function – which estimates the value of single states – is of crucial importance. In this paper, we investigate the use of case-based methods to realise that task. The approach we take is evaluated in a case study in robotic soccer simulation.
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References
Aha, D.: Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms. Journal of Man-Machine Studies 36(2), 267–287 (1992)
Bellman, R.E.: Dynamic Programming. Princeton University Press, USA (1957)
Bertsekas, D.P., Tsitsiklis, J.N.: Neuro Dynamic Programming. Athena Scientific, USA (1996)
Burkhard, H.D., Wendler, J., Meinert, T., Myritz, H., Sander, G.: AT Humboldt in RoboCup 1999. In: RoboCup, pp. 542–545 (1999)
Driessens, K., Ramon, J.: Relational Instance Based Regression for Relational RL. In: Proceedings of ICML 2003, Washington, pp. 123–130. AAAI Press, Menlo Park (2003)
Forbes, J., Andre, D.: Representations for Learning Control Policies. In: Proceedings of the ICML 2002 Workshop on Development of Representations, The University of New South Wales, pp. 7–14 (2002)
Gordon, G.J.: Stable Function Approximation in Dynamic Programming. In: Proceedings of ICML 1995, San Francisco, pp. 261–268. Morgan Kaufmann, San Francisco (1995)
Kelly, J.D., Davis, L.: A Hybrid Genetic Algorithm for Classification. In: Proceedings of the Twefth International Joint Conference on Artificial Intelligence (IJCAI 1991), Sydney, Australia, pp. 645–650. Morgan Kaufmann, San Francisco (1991)
Kuhlmann, G., Stone, P.: Progress in Learning 3 vs. 2 Keepaway. In: RoboCup-2003: Robot Soccer World Cup VII, Berlin. Springer, Heidelberg (2004)
Merke, A., Riedmiller, M.: Karlsruhe Brainstromers – A Reinforcement Learning Way to Robotic Soccer II. In: RoboCup 2001: Robot Soccer World Cup (2001)
Noda, I., Matsubara, H., Hiraki, K., Frank, I.: Soccer Server: A Tool for Research on Multi-Agent Systems. Applied Artificial Intelligence 12(2-3), 233–250 (1998)
Ormoneit, D., Sen, S.: Kernel-Based Reinforcement Learning. Technical Report TR 1999-8, Statistics Institute, Stanford University, USA (1999)
Peng, J.: Efficient Memory-Based Dynamic Programming. In: 12th International Conference on Machine Learning, USA, pp. 438–446. Morgan Kaufmann, San Francisco (1995)
Ratitch, B., Precup, D.: Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 347–358. Springer, Heidelberg (2004)
Riedmiller, M., Braun, H.: A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN), San Francisco, USA, pp. 586–591 (1993)
Santamaria, J., Sutton, R., Ram, A.: Experiments with RL in Problems with Continuous State and Action Spaces. Adaptive Behavior 6(2), 163–217 (1998)
Smart, W.D., Kaelbling, L.P.: Practical Reinforcement Learning in Continuous Spaces. In: Proceedings of the 17th International Conference on Machine Learning (ICML 2000), San Francisco, USA. Morgan Kaufmann, San Francisco (2000)
Stahl, A., Gabel, T.: Using Evolution Programs to Learn Local Similarity Measures. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 537–551. Springer, Heidelberg (2003)
Stolzenburg, F., Obst, O., Murray, J.: Qualitative Velocity and Ball Interception. In: Advances in AI, 25th German Conference on AI, Aachen, pp. 283–298 (2002)
Sutton, R.S.: Learning to Predict by the Methods of Temporal Differences. Machine Learning 3, 9–44 (1988)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. An Introduction. MIT Press/A Bradford Book, Cambridge (1998)
Veloso, M., Balch, T., Stone, P., et al.: RoboCup 2001: The Fifth Robotic Soccer World Championships. AI Magazine 1(23), 55–68 (2002)
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Gabel, T., Riedmiller, M. (2005). CBR for State Value Function Approximation in Reinforcement Learning. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_18
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DOI: https://doi.org/10.1007/11536406_18
Publisher Name: Springer, Berlin, Heidelberg
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