10 papers
Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity
Loïc Cabannes, Pierre-Emmanuel Mazaré, Gergely Szilvasy +6
Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention models fall behind in long-cont…
Survival Reinforcement Learning: Toward Scalable Self-Supervised RL
Franki Nguimatsia-Tiofack, Fabian Schramm, Théotime Le Hellard +1
While self-supervised Contrastive Reinforcement Learning (CRL) has shown remarkable depth-scaling capabilities, successfully using networks over 64 layers, scaled CRL still struggl…
SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
Franki Nguimatsia Tiofack, Fabian Schramm, Théotime Le Hellard +1
Standard approaches to goal-conditioned reinforcement learning (GCRL) that rely on temporal-difference learning can be unstable and sample-inefficient due to bootstrapping. While r…
Variance-Reduced Model Predictive Path Integral via Quadratic Model Approximation
Fabian Schramm, Franki Nguimatsia Tiofack, Nicolas Perrin-Gilbert +2
Sampling-based controllers, such as Model Predictive Path Integral (MPPI) methods, offer substantial flexibility but often suffer from high variance and low sample efficiency. To a…
Accelerating trajectory optimization with Sobolev-trained diffusion policies
Théotime Le Hellard, Franki Nguimatsia Tiofack, Quentin Le Lidec +1
Trajectory Optimization (TO) solvers exploit known system dynamics to compute locally optimal trajectories through iterative improvements. A downside is that each new problem insta…
Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming
Antoine Groudiev, Fabian Schramm, Ãloïse Berthier +2
Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical routines. Among recent advances,…