Name
Title | ||
|---|---|---|
Policy Approximation in Policy Iteration Approximate Dynamic Programming for Discrete-Time Nonlinear Systems. |
Abstract | ||
|---|---|---|
Policy iteration approximate dynamic programming (DP) is an important algorithm for solving optimal decision and control problems. In this paper, we focus on the problem associated with policy approximation in policy iteration approximate DP for discrete-time nonlinear systems using infinite-horizon undiscounted value functions. Taking policy approximation error into account, we demonstrate asympt... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2702566 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Approximation algorithms,Approximation error,Convergence,Optimal control,Nonlinear systems,Discrete-time systems | Approximation algorithm,Dynamic programming,Mathematical optimization,Optimal control,Computer science,Bellman equation,Volterra series,Exponential stability,Approximation error,Bounded function | Journal |
Volume | Issue | ISSN |
29 | 7 | 2162-237X |
Citations | PageRank | References |
3 | 0.39 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentao Guo | 1 | 54 | 4.60 |
| Jennie Si | 2 | 746 | 70.23 |
| Feng Liu | 3 | 269 | 28.35 |
| Shengwei Mei | 4 | 196 | 34.27 |