5 papers · 1 filter
How Hard is it to Confuse a World Model?
Waris Radji, Odalric-Ambrym Maillard
In reinforcement learning (RL) theory, the concept of most confusing instances is central to establishing regret lower bounds, that is, the minimal exploration needed to solve a pr…
The Confusing Instance Principle for Online Linear Quadratic Control
Waris Radji, Odalric-Ambrym Maillard
We revisit the problem of controlling linear systems with quadratic cost under unknown dynamics with model-based reinforcement learning. Traditional methods like Optimism in the Fa…
Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX
Waris Radji, Thomas Michel, Hector Piteau
Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable. While modern video games may offer rich dynamics, they are com…
Offline Goal-Conditioned Reinforcement Learning with Projective Quasimetric Planning
Anthony Kobanda, Waris Radji, Mathieu Petitbois +2
Offline Goal-Conditioned Reinforcement Learning seeks to train agents to reach specified goals from previously collected trajectories. Scaling that promises to long-horizon tasks r…
Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs
Hector Kohler, Quentin Delfosse, Waris Radji +2
There exist applications of reinforcement learning like medicine where policies need to be ''interpretable'' by humans. User studies have shown that some policy classes might be mo…