4 citations · 9 across the 18 of their papers we have counts for
4 papers · 1 filter
Some remarks on stochastic converse Lyapunov theorems
Pavel Osinenko, Grigory Yaremenko
In this brief note, we investigate some constructions of Lyapunov functions for stochastic discrete-time stabilizable dynamical systems, in other words, controlled Markov chains. T…
A note on stabilizing reinforcement learning
Pavel Osinenko, Grigory Yaremenko, Ilya Osokin
Reinforcement learning is a general methodology of adaptive optimal control that has attracted much attention in various fields ranging from video game industry to robot manipulato…
Effects of sampling and horizon in predictive reinforcement learning
Pavel Osinenko, Dmitrii Dobriborsci
Plain reinforcement learning (RL) may be prone to loss of convergence, constraint violation, unexpected performance, etc. Commonly, RL agents undergo extensive learning stages to a…
On stochastic stabilization of sampled systems
Pavel Osinenko, Grigory Yaremenko
This paper addresses stochastic stabilization in case where implementation of control policies is digital, i. e., when the dynamical system is treated continuous, whereas the contr…