4 citations · 9 across the 18 of their papers we have counts for
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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…