7 papers
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…
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,…
Reference-Free Sampling-Based Model Predictive Control
Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert +1
We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences.…
Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
Franki Nguimatsia Tiofack, Théotime Le Hellard, Fabian Schramm +2
Offline reinforcement learning often relies on behavior regularization that enforces policies to remain close to the dataset distribution. However, such approaches fail to distingu…