2 papers
cs.LG2025
Theoretical Barriers in Bellman-Based Reinforcement Learning
Brieuc Pinon, Raphaël Jungers, Jean-Charles Delvenne
Reinforcement Learning algorithms designed for high-dimensional spaces often enforce the Bellman equation on a sampled subset of states, relying on generalization to propagate know…
cs.LG2023
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned
Brieuc Pinon, Raphaël Jungers, Jean-Charles Delvenne
We prove a fundamental limitation on the efficiency of a wide class of Reinforcement Learning (RL) algorithms. This limitation applies to model-free RL methods as well as a broad r…