Computer Science > Systems and Control
[Submitted on 23 Feb 2016 (v1), last revised 30 Aug 2018 (this version, v3)]
Title:Performance guarantees for model-based Approximate Dynamic Programming in continuous spaces
View PDFAbstract:We study both the value function and Q-function formulation of the Linear Programming approach to Approximate Dynamic Programming. The approach is model-based and optimizes over a restricted function space to approximate the value function or Q-function. Working in the discrete time, continuous space setting, we provide guarantees for the fitting error and online performance of the policy. In particular, the online performance guarantee is obtained by analyzing an iterated version of the greedy policy, and the fitting error guarantee by analyzing an iterated version of the Bellman inequality. These guarantees complement the existing bounds that appear in the literature. The Q-function formulation offers benefits, for example, in decentralized controller design, however it can lead to computationally demanding optimization problems. To alleviate this drawback, we provide a condition that simplifies the formulation, resulting in improved computational times.
Submission history
From: Paul Beuchat [view email][v1] Tue, 23 Feb 2016 19:42:48 UTC (727 KB)
[v2] Tue, 29 Mar 2016 20:10:57 UTC (727 KB)
[v3] Thu, 30 Aug 2018 07:43:51 UTC (803 KB)
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