The Linear Programming Approach to Approximate Dynamic Programming
TLDR
In this article, an efficient method based on linear programming for approximating solutions to large-scale stochastic control problems is proposed. But the approach is not suitable for large scale queueing networks.Abstract:
The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach "fits" a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We develop error bounds that offer performance guarantees and also guide the selection of both basis functions and "state-relevance weights" that influence quality of the approximation. Experimental results in the domain of queueing network control provide empirical support for the methodology.read more
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Chapter 5 – Dynamic asset allocation strategies using a stochastic dynamic programming aproach
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Control design for specifications on stochastic hybrid systems
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Automata theory meets approximate dynamic programming: Optimal control with temporal logic constraints
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Reinforcement Learning and Approximate Dynamic Programming (RLADP)—Foundations, Common Misconceptions, and the Challenges Ahead
Frank L. Lewis,Derong Liu +1 more
TL;DR: In this article, the authors introduce RLADP and present some basic challenges in implementing ADP implementation, including the use of ADP-based ADP references and their implementation.
References
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