Actor-Critic Off-Policy Learning for Optimal Control of Multiple-Model Discrete-Time Systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43949872" target="_blank" >RIV/49777513:23520/18:43949872 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TCYB.2016.2618926" target="_blank" >http://dx.doi.org/10.1109/TCYB.2016.2618926</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCYB.2016.2618926" target="_blank" >10.1109/TCYB.2016.2618926</a>

Result language
angličtina
Original language name
Actor-Critic Off-Policy Learning for Optimal Control of Multiple-Model Discrete-Time Systems
Original language description
In this paper, motivated by human neurocognitive experiments, a model-free off-policy reinforcement learning algorithm is developed to solve the optimal tracking control of multiple-model linear discrete-time systems. First, an adaptive self-organizing map neural network is used to determine the system behavior from measured data and to assign a responsibility signal to each of system possible behaviors. A new model is added if a sudden change of system behavior is detected from the measured data and the behavior has not been previously detected. A value function is represented by partially weighted value functions. Then, the off-policy iteration algorithm is generalized to multiple-model learning to find a solution without any knowledge about the system dynamics or reference trajectory dynamics. The off-policy approach helps to increase data efficiency and speed of tuning since a stream of experiences obtained from executing a behavior policy is reused to update several value functions corresponding to different learning policies sequentially. Two numerical examples serve as a demonstration of the off-policy algorithm performance.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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