Accelerated value iteration via Anderson mixing

2021 ◽  
Vol 64 (12) ◽  
Author(s):  
Yujun Li
Keyword(s):  
Value Iteration

Autonomous Soaring Policy Initialization Through Value Iteration

2021 ◽  
Author(s):  
Benjamin J. Rothaupt ◽  
Stefan Notter ◽  
Walter Fichter
Keyword(s):  
Value Iteration ◽  
Autonomous Soaring

scholarly journals A Nearer Optimal and Faster Trained Value Iteration ADP for Discrete-Time Nonlinear Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14933-14944
Author(s):  
Junping Hu ◽  
Gen Yang ◽  
Zhicheng Hou ◽  
Gong Zhang ◽  
Wenlin Yang ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Value Iteration

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

2005 ◽  
Vol 3 (1) ◽  
Author(s):  
Damien Ernst ◽  
Mevludin Glavic ◽  
Pierre Geurts ◽  
Louis Wehenkel
Keyword(s):  
Reinforcement Learning ◽  
Power System ◽  
Control Problem ◽  
Learning Algorithm ◽  
Electrical Power ◽  
Complex Case ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Learning Context ◽  
Power System Control

In this paper we explain how to design intelligent agents able to process the information acquired from interaction with a system to learn a good control policy and show how the methodology can be applied to control some devices aimed to damp electrical power oscillations. The control problem is formalized as a discrete-time optimal control problem and the information acquired from interaction with the system is a set of samples, where each sample is composed of four elements: a state, the action taken while being in this state, the instantaneous reward observed and the successor state of the system. To process this information we consider reinforcement learning algorithms that determine an approximation of the so-called Q-function by mimicking the behavior of the value iteration algorithm. Simulations are first carried on a benchmark power system modeled with two state variables. Then we present a more complex case study on a four-machine power system where the reinforcement learning algorithm controls a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations.

A Model-Based Factored Bayesian Reinforcement Learning Approach

2014 ◽  
Vol 513-517 ◽  
pp. 1092-1095
Author(s):  
Bo Wu ◽  
Yan Peng Feng ◽  
Hong Yan Zheng
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Practical Applications ◽  
Model Based ◽  
Online Planning ◽  
Bayesian Reinforcement Learning ◽  
Bayesian Inference Method ◽  
Unknown Structure

Bayesian reinforcement learning has turned out to be an effective solution to the optimal tradeoff between exploration and exploitation. However, in practical applications, the learning parameters with exponential growth are the main impediment for online planning and learning. To overcome this problem, we bring factored representations, model-based learning, and Bayesian reinforcement learning together in a new approach. Firstly, we exploit a factored representation to describe the states to reduce the size of learning parameters, and adopt Bayesian inference method to learn the unknown structure and parameters simultaneously. Then, we use an online point-based value iteration algorithm to plan and learn. The experimental results show that the proposed approach is an effective way for improving the learning efficiency in large-scale state spaces.

Sign in / Sign up

Export Citation Format

Share Document