Dirk Ormoneit and Peter W. Glynn, Kernel-based reinforcement learning in average-cost problems: An application to optimal portfolio choice, Advances in Neural Information Processing Systems, The MIT Press, 2000. Home/Search Document Details and Download
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Kernel-Based Reinforcement Learning in Average-Cost Problems: .. - Ormoneit, Glynn (2000) (2 citations) Self-citation (Ormoneit Glynn) (Correct)
....therefore much more widely applicable in practice. While our method addresses both discounted and average cost problems, we focus on average costs here and refer the reader interested in discounted costs to [OS00] For brevity, we also defer technical details and proofs to an accompanying paper [OG00]. Note that averagecost reinforcement learning has been discussed by several authors (e.g. TR99] The remainder of this work is organized as follows. In Section 2 be provide basic definitions and we describe the kernel based reinforcement learning algorithm. Section 3 focuses on the practical ....
....action a in state x. Strategies, policies, or controls are understood as mappings of the form : IR A, and we let P x; denote the probability distribution governing the Markov chain starting from X 0 = x associated with the policy . Several regularity conditions are listed in detail in [OG00]. Our goal is to identify policies that are optimal in that they minimize the long run average cost j j lim T 1 E x; T t=0 c(X t ; X t ) An optimal policy, can be characterized as a solution to the Average Cost Optimality Equation (ACOE) h (x) min ) x)g; 1) ....
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D. Ormoneit and P. Glynn. Kernel-based reinforcement learning in averagecost problems. Working paper, Stanford University. In preparation.
Kernel-Based Reinforcement Learning - Ormoneit, Sen (1999) (7 citations) Self-citation (Ormoneit) (Correct)
.... in Finance, see Longsta and Schwartz s paper on American option pricing [15] and Brandt s work on Optimal Portfolio Choice [7] While our method addresses both discounted and average cost problems, we focus on discounted costs here and refer the reader interested in average costs to other work [16, 17]. The remainder of this work is organized as follows. In Section 2, we review basic facts about Markov Decision Processes. In Sections 3 and 4, we introduce the kernel based reinforcement learning operator and discuss algorithmic considerations relevant for its practical application. In Section 5 ....
D. Ormoneit and P. Glynn. Kernel-based reinforcement learning in average-cost problems: An application to optimal portfolio choice, 2000. Working Paper, Department of Statistics, Stanford University.
Reinforcement Learning by Policy Search - Peshkin (2001) (7 citations) (Correct)
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Dirk Ormoneit and Peter W. Glynn, Kernel-based reinforcement learning in average-cost problems: An application to optimal portfolio choice, Advances in Neural Information Processing Systems, The MIT Press, 2000.
Policy Search in Kernel Hilbert Space - Andrew Bagnell And (2003) (Correct)
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D. Ormoneit and P. Glynn. Kernel-based reinforcement learning in average cost problems: An application to optimal portfolio choice. In Advances in Neural Information Processing Systems 13, Cambridge, MA, 2001. MIT Press.
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