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COLT
2000
Springer

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Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning

15 years 3 months ago

Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning

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We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process ( ¢¡¤£¦¥§ ), and focus on gradient ascent approaches to this problem. In [3] we introduced ¨ ¢¡¤£¦¥§ , an algorithm for estimating the performance gradient of a ©¡¤£¦¥¤ from a single sample path, and we proved that this algorithm almost surely converges to an approximation to the gradient. In this paper, we provide a convergence rate for the estimates produced by ¨ ¢¡¤£¦¥§ , and give an improved bound on the approximation error of these estimates. Both of these bounds are in terms of mixing times of the ©¡¤£¦¥¤ .

Peter L. Bartlett, Jonathan Baxter

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COLT 2000 | Gradient Ascent Approaches | Machine Learning | Partially Observable Markov Decision Process | Single Sample Path |

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Added	02 Aug 2010
Updated	02 Aug 2010
Type	Conference
Year	2000
Where	COLT
Authors	Peter L. Bartlett, Jonathan Baxter

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