1 citations · 1 across the 5 of their papers we have counts for
5 papers
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 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…