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cs.LG2026
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…
cs.LG2021★ 4 cited
On the Turnpike to Design of Deep Neural Nets: Explicit Depth Bounds
Timm Faulwasser, Arne-Jens Hempel, Stefan Streif
It is well-known that the training of Deep Neural Networks (DNN) can be formalized in the language of optimal control. In this context, this paper leverages classical turnpike prop…