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On the Convergence of Stochastic Iterative Dynamic Programming Algorithms

Author(s)
Jaakkola, Tommi; Jordan, Michael I.; Singh, Satinder P.
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Abstract
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(lambda) and Q-learning belong.
Date issued
1993-08-01
URI
http://hdl.handle.net/1721.1/7205
Other identifiers
AIM-1441
CBCL-084
Series/Report no.
AIM-1441CBCL-084
Keywords
reinforcement learning, stochastic approximation, sconvergence, dynamic programming

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  • CBCL Memos (1993 - 2004)

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