1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
A model-based approach to meta-Reinforcement Learning: Transformers and tree search
Brieuc Pinon, Jean-Charles Delvenne, Raphaël Jungers
Meta-learning is a line of research that develops the ability to leverage past experiences to efficiently solve new learning problems. Meta-Reinforcement Learning (meta-RL) methods…
PAC-learning gains of Turing machines over circuits and neural networks
Brieuc Pinon, Raphaël Jungers, Jean-Charles Delvenne
A caveat to many applications of the current Deep Learning approach is the need for large-scale data. One improvement suggested by Kolmogorov Complexity results is to apply the min…