5 papers
An Agency-Transferring Model-Free Policy Enhancement Technique
Anton Bolychev, Georgiy Malaniya, Sinan Ibrahim +1
Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation. Yet many con…
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…
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…