Book Review: Discrete-time Markov control processes: Basic optimality criteria
By Wendell H. Fleming. 1997
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Didier Henrion
- Stephen P. Boyd
- Yang Wang
- Jean B. Lasserre
- Brendan O'Donoghue
- Christophe Prieur
- Emmanuel Trélat
- Monique Laurent
Citations in the corpus
- Optimal Management and Sizing of Energy Storage Under Dynamic Pricing for the Efficient Integration of Renewable Energy (2015, methodology): “Finally, we extend the results to the average cost problem using the vanishing discount approach (see [21]) that characterizes the asymptotic behavior of the cost as the discount factor approaches one.”
- Optimality Conditions for Inventory Control (2016, background): “The reader may find coverage of these topics in books on MDPs (Bäuerle and Rieder [3], Bertsekas and Shreve [12], Dynkin and Yushkevich [22], Feinberg and Shwartz [29], Heyman and Sobel [42], Hernández-Lerma [38], Hernández-Lerma and Lasserre [40, 41], Puterman [47], Sennott [54]) and on inventory m”
- Risk-averse dynamic programming for Markov decision processes (2010, background): “(2) Under more specific but still fairly general assumptions, both problems have solutions in form of Markov policies (see, e. g., [21]).”
- Nonlinear Optimal Control via Occupation Measures and LMI-Relaxations (2007, methodology): “From Banach-Alaoglu Theorem, B 1 is compact for the weak ⋆ topology, and even sequentially compact because B 1 is metrizable (see, e.g., Hern´andez-Lerma and Lasserre [22, Lemma 1.3.2]).”
- Performance bounds for linear stochastic control (2009, background): “(Projected linear state feedback is sometimes called saturated linear state feedback in this case.)”
- Optimal energy management policies for energy harvesting sensor nodes (2008, background): “Not only we get existence of α-discount optimal policy, from [25], we also get vn(q, e) → vα(q, e) as n→ ∞ where vn(q, e) is n-step optimal α-discount cost.”
- Average optimality for Markov decision processes in borel spaces: a new condition and approach (2006, methodology): “This is because the state spaces considered in the previous literature are all denumerable, except in [2], [7], [14], [13], [12], [19], and [22], where the cost functions are assumed to be bounded below.”