A Nearer Optimal and Faster Trained Value Iteration ADP for Discrete-Time Nonlinear Systems
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3051984
Abstract: Adaptive dynamic programming (ADP) is generally implemented using three neural networks: model network, action network, and critic network. In the conventional works of the value iteration ADP, the model network is initialized randomly and trained… read more here.
Keywords: value iteration; time; network; iteration adp ... See more keywords
Point-Based Value Iteration for VAR-POMDPs
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DOI: 10.1109/lcsys.2020.3046373
Abstract: Partially observable Markov decision processes have been widely adopted in the automatic planning literature since it elegantly captures both execution and observation uncertainties. In our previous paper, we proposed a model called vector autoregressive partially… read more here.
Keywords: based value; point based; value; value iteration ... See more keywords
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes
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DOI: 10.1109/lcsys.2020.3047729
Abstract: The problem of synthesizing an optimal sensor selection policy is pertinent to a variety of engineering applications ranging from event detection to autonomous navigation. We consider such a synthesis problem in the context of linear-Gaussian… read more here.
Keywords: sensor selection; value iteration; selection;
Online Value Iteration for Intelligent Discounted Tracking Design of Constrained Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2022.3172700
Abstract: In this brief, in order to solve the discounted optimal tracking control problem for discrete-time systems with control constraints, an advanced online value iteration (VI) algorithm is developed. First, we revisit discounted general value iteration… read more here.
Keywords: control; tracking control; value iteration; online value ... See more keywords
Boundary Optimal Control for Parabolic Distributed Parameter Systems With Value Iteration.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2022.3224485
Abstract: A reinforcement learning-based boundary optimal control algorithm for parabolic distributed parameter systems is developed in this article. First, a spatial Riccati-like equation and an integral optimal controller are derived in infinite-time horizon based on the… read more here.
Keywords: optimal control; boundary optimal; value; value iteration ... See more keywords
A Novel Value Iteration Scheme With Adjustable Convergence Rate.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3143527
Abstract: In this article, a novel value iteration scheme is developed with convergence and stability discussions. A relaxation factor is introduced to adjust the convergence rate of the value function sequence. The convergence conditions with respect… read more here.
Keywords: convergence; value; value iteration; iteration scheme ... See more keywords
Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Convergence Analysis
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DOI: 10.1109/tsmc.2016.2623766
Abstract: In this paper, convergence properties are established for the newly developed discrete-time local value iteration adaptive dynamic programming (ADP) algorithm. The present local iterative ADP algorithm permits an arbitrary positive semidefinite function to initialize the… read more here.
Keywords: time; value; function; local value ... See more keywords
Balancing Value Iteration and Policy Iteration for Discrete-Time Control
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2019.2898389
Abstract: The optimal control problem of discrete-time nonlinear systems depends on the solution of the Bellman equation. In this paper, an adaptive reinforcement learning (RL) method is developed to solve the complex Bellman equation, which balances… read more here.
Keywords: discrete time; iteration policy; control; iteration ... See more keywords