4 citations · 9 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
A study of first-passage time minimization via Q-learning in heated gridworlds
M. A. Larchenko, P. Osinenko, G. Yaremenko +1
Optimization of first-passage times is required in applications ranging from nanobots navigation to market trading. In such settings, one often encounters unevenly distributed nois…
An experimental study of two predictive reinforcement learning methods and comparison with model-predictive control
Dmitrii Dobriborsci, Pavel Osinenko
Reinforcement learning (RL) has been successfully used in various simulations and computer games. Industry-related applications, such as autonomous mobile robot motion control, are…
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…
A generalized stacked reinforcement learning method for sampled systems
Pavel Osinenko, Dmitrii Dobriborsci, Grigory Yaremenko +1
A common setting of reinforcement learning (RL) is a Markov decision process (MDP) in which the environment is a stochastic discrete-time dynamical system. Whereas MDPs are suitabl…
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…