Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Johan Samir Obando Ceron, le 13 novembre à 9h00 (à distance).

Titre : Maximizing Learning, Minimizing Waste: The Art of Efficient Deep RL

Date: jeudi 13 novembre à 9h

Location: ZOOM

Jury

Résumé

Deep reinforcement learning (RL) has achieved remarkable success but remains unstable and inefficient at scale. This thesis will investigate how modular architectures, sparsity, and stable optimization jointly enable scalable and reliable deep RL. First, Mixtures of Experts Unlock Parameter Scaling for Deep RL demonstrates that adding Soft MoE modules to value networks yields consistent performance gains as parameter counts increase, revealing effective parameter scaling when capacity is organized modularly.

Next, In Value-Based Deep Reinforcement Learning, a Pruned Network is a Good Network we show that gradual magnitude pruning can uncover sparse, high-performing subnetworks that outperform dense baselines while using only a fraction of the parameters—benefits that grow with model size.

Finally, Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning identifies gradient pathologies arising from interactions with RL non-stationarity as a key cause of scaling failures, and introduces simple architectural and optimization interventions that preserve gradient flow, enabling robust training of large models. Together, these works establish a unified view of how architectural scaling, gradient stability, and sparsity interact—offering a path toward reliable, large-scale deep reinforcement learning.