2 citations · 2 across the 12 of their papers we have counts for
3 papers · 1 filter
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,…
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…