9 citations · 14 across the 8 of their papers we have counts for
8 papers
Exploration by Learning Diverse Skills through Successor State Measures
Paul-Antoine Le Tolguenec, Yann Besse, Florent Teichteil-Konigsbuch +2
The ability to perform different skills can encourage agents to explore. In this work, we aim to construct a set of diverse skills which uniformly cover the state space. We propose…
RRLS : Robust Reinforcement Learning Suite
Adil Zouitine, David Bertoin, Pierre Clavier +2
Robust reinforcement learning is the problem of learning control policies that provide optimal worst-case performance against a span of adversarial environments. It is a crucial in…
Time-Constrained Robust MDPs
Adil Zouitine, David Bertoin, Pierre Clavier +2
Robust reinforcement learning is essential for deploying reinforcement learning algorithms in real-world scenarios where environmental uncertainty predominates. Traditional robust…
Bootstrapping Expectiles in Reinforcement Learning
Pierre Clavier, Emmanuel Rachelson, Erwan Le Pennec +1
Many classic Reinforcement Learning (RL) algorithms rely on a Bellman operator, which involves an expectation over the next states, leading to the concept of bootstrapping. To intr…
On the integration of Dantzig-Wolfe and Fenchel decompositions via directional normalizations
François Lamothe, Alain Haït, Emmanuel Rachelson +2
The strengthening of linear relaxations and bounds of mixed integer linear programs has been an active research topic for decades. Enumeration-based methods for integer programming…
Dynamic unsplittable flows with path-change penalties: new formulations and solution schemes for large instances
François Lamothe, Emmanuel Rachelson, Alain Haït +2
In this work, we consider the dynamic unsplittable flow problem. This variation of the unsplittable flow problem has received little attention so far. The unsplittable flow problem…