4 citations · 7 across the 15 of their papers we have counts for
3 papers · 1 filter
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…
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…