4 citations · 9 across the 18 of their papers we have counts for
19 papers
Continuous-Time Critic as a Lyapunov Function: Projected Adaptation and Cart-Pole Stabilization
Pavel Osinenko
We study adaptive stabilization of known control-affine systems by treating the current parameterized critic itself as a Lyapunov candidate. Its total derivative contains a paramet…
SwarmRaft: Leveraging Consensus for Robust Drone Swarm Coordination in GNSS-Degraded Environments
Kapel Dev, Yash Madhwal, Sofia Shevelo +2
Unmanned aerial vehicle (UAV) swarms are increasingly used in critical applications such as aerial mapping, environmental monitoring, and autonomous delivery. However, the reliabil…
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 universal policy wrapper with guarantees
Anton Bolychev, Georgiy Malaniya, Grigory Yaremenko +2
We introduce a universal policy wrapper for reinforcement learning agents that ensures formal goal-reaching guarantees. In contrast to standard reinforcement learning algorithms th…
Multi-CALF: A Policy Combination Approach with Statistical Guarantees
Georgiy Malaniya, Anton Bolychev, Grigory Yaremenko +2
We introduce Multi-CALF, an algorithm that intelligently combines reinforcement learning policies based on their relative value improvements. Our approach integrates a standard RL…
A novel agent with formal goal-reaching guarantees: an experimental study with a mobile robot
Grigory Yaremenko, Dmitrii Dobriborsci, Roman Zashchitin +3
Reinforcement Learning (RL) has been shown to be effective and convenient for a number of tasks in robotics. However, it requires the exploration of a sufficiently large number of…
Critic as Lyapunov function (CALF): a model-free, stability-ensuring agent
Pavel Osinenko, Grigory Yaremenko, Roman Zashchitin +3
This work presents and showcases a novel reinforcement learning agent called Critic As Lyapunov Function (CALF) which is model-free and ensures online environment, in other words,…
Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
Sinan Ibrahim, Mostafa Mostafa, Ali Jnadi +2
The aim of Reinforcement Learning (RL) in real-world applications is to create systems capable of making autonomous decisions by learning from their environment through trial and e…
An agent design with goal reaching guarantees for enhancement of learning
Pavel Osinenko, Grigory Yaremenko, Georgiy Malaniya +2
Reinforcement learning is commonly concerned with problems of maximizing accumulated rewards in Markov decision processes. Oftentimes, a certain goal state or a subset of the state…
Combining model-predictive control and predictive reinforcement learning for stable quadrupedal robot locomotion
Vyacheslav Kovalev, Anna Shkromada, Henni Ouerdane +1
Stable gait generation is a crucial problem for legged robot locomotion as this impacts other critical performance factors such as, e.g. mobility over an uneven terrain and power c…
A stabilizing reinforcement learning approach for sampled systems with partially unknown models
Lukas Beckenbach, Pavel Osinenko, Stefan Streif
Reinforcement learning is commonly associated with training of reward-maximizing (or cost-minimizing) agents, in other words, controllers. It can be applied in model-free or model-…
A framework for online, stabilizing reinforcement learning
Grigory Yaremenko, Georgiy Malaniya, Pavel Osinenko
Online reinforcement learning is concerned with training an agent on-the-fly via dynamic interaction with the environment. Here, due to the specifics of the application, it is not…
On stochastic stabilization via non-smooth control Lyapunov functions
Pavel Osinenko, Grigory Yaremenko, Georgiy Malaniya
Control Lyapunov function is a central tool in stabilization. It generalizes an abstract energy function -- a Lyapunov function -- to the case of controlled systems. It is a known…
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…