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On-Line Q-Learning Using Connectionist Systems (1994)
| Citations: | 380 - 1 self |
BibTeX
@TECHREPORT{Rummery94on-lineq-learning,
author = {G. A. Rummery and M. Niranjan},
title = {On-Line Q-Learning Using Connectionist Systems},
institution = {},
year = {1994}
}
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Abstract
Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finite-state Markovian problems, which is too restrictive for many real-world environments. Therefore, it is desirable to extend these methods to high dimensional continuous state-spaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of back-propagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number of different algorithms based around Q-Learning (Watkins 1989) combined with the Temporal Difference algorithm (Sutton 1988), including a new algorithm (Modified Connectionist Q-Learning), and Q() (Peng and Williams 1994). In addition, we present algorithms for applying these updates on-line during trials, unlike backward replay used by Lin (1993) that requires waiting until the end of each t...
Keyphrases
on-line q-learning using connectionist system backward replay high dimensional continuous state-spaces information learnt function approximation reinforcement learning algorithm different algorithm many real-world environment temporal difference algorithm new algorithm powerful machine learning technique back-propagation neural network modified connectionist q-learning finite-state markovian problem
