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Abstract: Classifier systems are genetics-based learning systems using the paradigm of reinforcement learning. In the most challenging case of delayed reinforcement, it involves a difficult temporal credit assignment problem. Standard classifier systems solve this problem using the bucket brigade algorithm. In this paper we show how to make the temporal credit assignment process faster by augmenting this algorithm by some refinements borrowed from a related field of reinforcement learning algorithms... (Update)
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BibTeX entry: (Update)
@inproceedings{ cichosz96faster,
author = "Pawel Cichosz and Jan J. Mulawka",
title = "Faster temporal credit assignment in learning classifier systems",
booktitle = "Proceedings of the First Polish Conference on Evolutionary Algorithms ({KAE}-96)",
year = "1996",
url = "citeseer.ist.psu.edu/82697.html" }
Citations (may not include all citations):658 Learning from Delayed Rewards (context) - Watkins - 1989
563 Learning to predict by the methods of temporal differences - Sutton - 1988
141 Temporal Credit Assignment in Reinforcement Learning (context) - Sutton - 1984
101 Reinforcement Learning for Robots Using Neural Networks (context) - Lin - 1993
95 Classifier systems and genetic algorithms (context) - Booker, Goldberg et al. - 1989
23 Truncating temporal differences: On the efficient implementa.. - Cichosz - 1995
21 learning and classifier systems (context) - Dorigo, Bersini - 1994
8 Fast and efficient reinforcement learning with truncated tem.. - Cichosz, Mulawka - 1995
7 Reinforcement learning algorithms based on the methods of te.. (context) - Cichosz - 1994
5 ZCS: A zeroth order classifier system (context) - Wilson - 1994
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Integrated Learning and Planning Based on Truncating Temporal.. - Cichosz (Correct)
Truncated Temporal Differences with Function Approximation.. - Cichosz (1996) (Correct)
An Analysis of Experience Replay in Temporal Difference Learning - Cichosz (Correct)
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