Performance Bounds in $L_p$‐norm for Approximate Value Iteration

Rémi Munos

doi:10.1137/040614384

Performance Bounds in $L_p$‐norm for Approximate Value Iteration

SIAM Journal on Control and Optimization ◽

10.1137/040614384 ◽

2007 ◽

Vol 46 (2) ◽

pp. 541-561 ◽

Cited By ~ 21

Author(s):

Rémi Munos

Keyword(s):

Value Iteration ◽

Performance Bounds ◽

Approximate Value Iteration

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Approximate value iteration with a fuzzy representation

Reinforcement Learning and Dynamic Programming Using Function Approximators ◽

10.1201/9781439821091-9 ◽

2017 ◽

pp. 131-180

Keyword(s):

Value Iteration ◽

Fuzzy Representation ◽

Approximate Value Iteration

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Feature-Discovering Approximate Value Iteration Methods

Lecture Notes in Computer Science - Abstraction, Reformulation and Approximation ◽

10.1007/11527862_25 ◽

2005 ◽

pp. 321-331 ◽

Cited By ~ 1

Author(s):

Jia-Hong Wu ◽

Robert Givan

Keyword(s):

Value Iteration ◽

Iteration Methods ◽

Approximate Value Iteration

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Exploiting Multi-step Sample Trajectories for Approximate Value Iteration

Advanced Information Systems Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-642-40988-2_8 ◽

2013 ◽

pp. 113-128

Author(s):

Robert Wright ◽

Steven Loscalzo ◽

Philip Dexter ◽

Lei Yu

Keyword(s):

Value Iteration ◽

Approximate Value Iteration

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A perturbation approach to approximate value iteration for average cost Markov decision processes with Borel spaces and bounded costs

Kybernetika ◽

10.14736/kyb-2019-1-0081 ◽

2019 ◽

pp. 81-113

Author(s):

Óscar Vega-Amaya ◽

Joaquín López-Borbón

Keyword(s):

Markov Decision Processes ◽

Average Cost ◽

Decision Processes ◽

Perturbation Approach ◽

Value Iteration ◽

Markov Decision ◽

Approximate Value Iteration

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Approximate value iteration with randomized policies

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187) ◽

10.1109/cdc.2000.912232 ◽

2002 ◽

Author(s):

D.P. de Farias ◽

B. Van Roy

Keyword(s):

Value Iteration ◽

Approximate Value Iteration ◽

Randomized Policies

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Performance Loss Bounds for Approximate Value Iteration with State Aggregation

Mathematics of Operations Research ◽

10.1287/moor.1060.0188 ◽

2006 ◽

Vol 31 (2) ◽

pp. 234-244 ◽

Cited By ~ 34

Author(s):

Benjamin Van Roy

Keyword(s):

Value Iteration ◽

Performance Loss ◽

State Aggregation ◽

Approximate Value Iteration

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Markov chain sparsification with independent sets for approximate value iteration

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) ◽

10.1109/allerton.2015.7447172 ◽

2015 ◽

Cited By ~ 2

Author(s):

Eduardo Pavez ◽

Nicolo Michelusi ◽

Aamir Anis ◽

Urbashi Mitra ◽

Antonio Ortega

Keyword(s):

Markov Chain ◽

Independent Sets ◽

Value Iteration ◽

Approximate Value Iteration

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Data-driven optimal stabilization for discrete-time nonlinear systems by approximate value iteration

53rd IEEE Conference on Decision and Control ◽

10.1109/cdc.2014.7040182 ◽

2014 ◽

Cited By ~ 1

Author(s):

Yongqiang Li ◽

Zhengsheng Hou ◽

Yuanjing Feng

Keyword(s):

Nonlinear Systems ◽

Discrete Time ◽

Data Driven ◽

Value Iteration ◽

Optimal Stabilization ◽

Approximate Value Iteration

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On the Existence of Fixed Points for Approximate Value Iteration and Temporal-Difference Learning

Journal of Optimization Theory and Applications ◽

10.1023/a:1004641123405 ◽

2000 ◽

Vol 105 (3) ◽

pp. 589-608 ◽

Cited By ~ 27

Author(s):

D. P. De Farias ◽

B. Van Roy

Keyword(s):

Fixed Points ◽

Temporal Difference ◽

Value Iteration ◽

Temporal Difference Learning ◽

Approximate Value Iteration

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Approximate Value Iteration in the Reinforcement Learning Context. Application to Electrical Power System Control.

ENERGYO ◽

10.1515/energyo.0034.00204 ◽

2018 ◽

Author(s):

Damien Ernst ◽

Mevludin Glavic ◽

Pierre Geurts ◽

Louis Wehenkel

Keyword(s):

Reinforcement Learning ◽

Power System ◽

Electrical Power ◽

System Control ◽

Value Iteration ◽

Learning Context ◽

Electrical Power System ◽

Power System Control ◽

Approximate Value Iteration

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