An analysis of experience replay in temporal difference learning

Cybernetics and Systems

Volumn 30, Issue 5, 1999, Pages 341-363

  Cichosz, Paweè ^a  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; LEARNING ALGORITHMS; NUMERICAL METHODS; SET THEORY; THEOREM PROVING;

REINFORCEMENT LEARNING; TEMPORAL DIFFERENCE LEARNING;

LEARNING SYSTEMS;

EID: 0032649518     PISSN: 01969722     EISSN: 10876553     Source Type: Journal    
DOI: 10.1080/019697299125127     Document Type: Article

References (18)

1. Barto, A. G., R. S. Sutton, and C. W. Anderson. 1983. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Systems, Man, and Cybernetics 13:835-846.
2. Cichosz, P. 1995. Truncating temporal differences: On the efficient implementation of TD(A) for reinforcement learning. Journal of Artificial Intelligence Research 2:287-318.
3. Cichosz, P. 1997. Reinforcement Learning by Truncating Temporal Differences. Ph.D. thesis, Warsaw University of Technology, Department of Electronics and Information Technology.
4. Cichosz, P. and Mulawka, J. J. 1995. Fast and efficient reinforcement learning with truncated temporal differences. In Proceedings of the Twelfth International Conference on Machine Learning (ML-95), pp. 99-107. San Francisco, CA: Morgan Kaufmann.
5. Kaelbling, L. P., M. L. Littman, and A. W. Moore. 1996. Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4:237-285.
6. Klopf, A. H. 1982. The Hedonistic Neuron: A Theory of Memory, Learning, and Intelligence. Washington D.C.: Hemisphere.
7. Lin, Long-Ji. 1993. Reinforcement Learning for Robots Using Neural Networks. Ph.D. thesis, Carnegie-Mellon University, School of Computer Science, Pittsburgh, PA.
8. Mahadevan, S. and J. Connell. 1992. Automatic programming of behavior-based robots using reinforcement learning. Artificial Intelligence 55:311-365.
9. Peng, J. and R. J. Williams. 1994. Incremental multi-step Q-learning. In Proc. Eleventh International Conference on Machine Learning (ML-94), pp. 226-232. San Francisco, CA: Morgan Kaufmann.
10. Rummery, G. A. and M. Niranjan. 1994. On-Line Q-Learning Using Connectionist Systems. Technical Report CUED /F-INFENG /TR 166, Cambridge University, Engineering Department.
11. Singh, S. P. and R. S. Sutton. 1996. Reinforcement learning with replacing eligibility traces. Machine Learning 22:123-158.
12. Sutton, R. S. 1984. Temporal Credit Assignment in Reinforcement Learning. Ph.D. thesis, University of Massachusetts, Department of Computer and Information Science, Boston, MA.
13. Sutton, R. S. 1988. Learning to predict by the methods of temporal differences. Machine Learning 3:9-44.
14. Sutton, R. S. 1996. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 8, pp. 1038-1044. Cambridge, MA: MIT Press. Morgan Kaufmann.
15. Sutton, R. S. and S. P. Singh. 1994. On step-size and bias in temporal-difference learning. In Proc. Eighth Yale Workshop on Adaptive and Learning Systems. Center for Systems Science, Yale University, New Haven, CT, pp. 31-36.
16. Tesauro, G. 1992. Practical issues in temporal difference learning. Machine Learning 8:257-277.
17. Watkins, C. J. C. H. 1989. Learning from Delayed Rewards. Ph.D. thesis, King’s College, Cambridge.
18. Watkins, C. J. C. H. and P. Dayan. 1992. Technical note: Q-learning. Machine Learning 8:279-292.