Abstract
The behaviour of reinforcement learning (RL) algorithms is best understood in completely observable, finite state- and action-space, discrete-time controlled Markov-chains. Robot-learning domains, on the other hand, are inherently infinite both in time and space, and moreover they are only partially observable. In this article we suggest a systematic design method whose motivation comes from the desire to transform the task-to-be-solved into a finite-state, discrete-time, “approximately” Markovian task, which is completely observable too. The key idea is to break up the problem into subtasks and design controllers for each of the subtasks. Then operating conditions are attached to the controllers (together the controllers and their operating conditions which are called modules) and possible additional features are designed to facilitate observability. A new discrete time-counter is introduced at the “module-level” that clicks only when a change in the value of one of the features is observed. The approach was tried out on a real-life robot. Several RL algorithms were compared and it was found that a model-based approach worked best. The learnt switching strategy performed equally well as a handcrafted version. Moreover, the learnt strategy seemed to exploit certain properties of the environment which could not have been seen in advance, which predicted the promising possibility that a learnt controller might overperform a handcrafted switching strategy in the future.
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References
M. Asada, S. Noda, S. Tawaratsumida, and K. Hosoda. Purposive behavior acquisition for a real robot by vision-based reinforcement learning. Machine Learning, 23:279–303, 1996.
A. Barto, S. J. Bradtke, and S. Singh. Learning to act using real-time dynamic programming. Artificial Intelligence, 1(72):81–138, 1995.
R. Bellman. Dynamic Programming. Princeton University Press, Princeton, New Jersey, 1957.
R. Brooks. Elephants don’t play chess. In Designing Autonomous Agents. Bradford-MIT Press, 1991.
T. Jaakkola, M. Jordan, and S. Singh. On the convergence of stochastic iterative dynamic programming algorithms. Neural Computation, 6(6):1185–1201, November 1994.
Z. Kalmár, C. Szepesvari, and A. Lorincz. Generalization in an autonomous agent. In Proc. of IEEE WCCI IGNN’94, volume 3, pages 1815–1817, Orlando, Florida, June 1994. IEEE Inc.
Z. Kalmar, C. Szepesvári, and A. Lorincz. Generalized dynamic concept model as a route to construct adaptive autonomous agents. Neural Network World, 5:353–360, 1995.
Z. Kalmar, C. Szepesvári, and A. Lorincz. Module based reinforcement learning: Experiments with a real robot. Machine Learning, 31:55–85, 1998. joint special issue on “Learning Robots” with the J. of Autonomous Robots;.
M. Littman. Algorithms for Sequential Decision Making. PhD thesis, Department of Computer Science, Brown University, February 1996. Also Technical Report CS-96-09.
M. Littman and C. Szepesvári. A Generalized Reinforcement Learning Model: Convergence and applications. In Int. Conf. on Machine Learning, pages 310–318, 1996.
M. L. Littman, A. Cassandra, and L. P. Kaelbling. Learning policies for partially observable environments: Scaling up. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 362–370, San Francisco, CA, 1995. Morgan Kaufmann.
P. Maes. A bottom-up mechanism for behavior selection in an artificial creature. In J. Meyer and S. Wilson, editors, Proc. of the First International Conference on Simulation of Adaptive Behavior. MIT Press, 1991.
S. Mahadevan and J. Connell. Automatic programming of behavior-based robots using reinforcement learning. Artificial Intelligence, 55:311–365, 1992.
M. Mataric. Reinforcement learning in the multi-robot domain. Autonomous Robots, 4, 1997.
S. Ross. Applied Probability Models with Optimization Applications. Holden Day, San Francisco, California, 1970.
S. Singh, T. Jaakkola, and M. Jordan. Learning without state-estimation in partially observable Markovian decision processes. In Proc. of the Eleventh Machine Learning Conference, pages pp. 284–292, 1995.
R. Sutton. Temporal Credit Assignment in Reinforcement Learning. PhD thesis, University of Massachusetts, Amherst, MA, 1984.
R. S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. Advances in Neural Information Processing Systems, 8, 1996.
C. Szepesvári and M. Littman. A unified analysis of value-function-based reinforcement-learning algorithms. Neural Computation, 1997. submitted.
C. Szepesvári and A. Lorincz. Behavior of an adaptive self-organizing autonomous agent working with cues and competing concepts. Adaptive Behavior, 2(2): 131–160, 1994.
S. Thrun. The role of exploration in learning control. Van Nostrand Rheinhold, Florence KY, 1992.
J. Tsitsiklis and B. Van Roy. An analysis of temporal difference learning with function approximation. Technical Report LIDS-P-2322, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, 1995.
J. N. Tsitsiklis and B. Van Roy. Feature-based methods for large scale dynamic programming. Machine Learning, 22:59–94, 1996.
E. Uchibe, M. Asada, and K. Hosoda. Behavior coordination for a mobile robot using modular reinforcement learning. In Proc. of IEEE/RSJ Int. Conf. on Intelligent Robot and Sytems, pages 1329–1336, 1996.
C. Watkins and P. Dayan. Q-learning. Machine Learning, 3(8):279–292, 1992.
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© 1998 Springer-Verlag Berlin Heidelberg
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Kalmár, Z., Szepesvári, C., Lorincz, A. (1998). Modular Reinforcement Learning: An Application to a Real Robot Task. In: Birk, A., Demiris, J. (eds) Learning Robots. EWLR 1997. Lecture Notes in Computer Science(), vol 1545. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49240-2_3
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DOI: https://doi.org/10.1007/3-540-49240-2_3
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