10 citations · 47 across the 17 of their papers we have counts for
4 papers · 1 filter
Ground-Truth-First Benchmarking of Reinforcement Learning via Converse Optimality: From Smooth, Fully Observed Systems to Non-Smooth or Partially Observed Tasks
Grégoire Ouerdane, Sinan Ibrahim, Stefan Streif +1
Benchmarking in reinforcement learning is usually relative: algorithms are ranked by returns on environments with unknown optima. Distance to optimality remains unobservable, while…
Some Essential Constructive Foundations for Systems and Control
Pavel Osinenko
This work develops several constructive foundations for systems and control within Bishop-style constructive mathematics. For an engineer, the guiding principle is that an object c…
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…
Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
Sinan Ibrahim, Grégoire Ouerdane, Hadi Salloum +3
The objective comparison of Reinforcement Learning (RL) algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sen…