2 citations · 4 across the 3 of their papers we have counts for
5 papers
Local Feature Swapping for Generalization in Reinforcement Learning
David Bertoin, Emmanuel Rachelson
Over the past few years, the acceleration of computing resources and research in deep learning has led to significant practical successes in a range of tasks, including in particul…
Lipschitz Lifelong Reinforcement Learning
Erwan Lecarpentier, David Abel, Kavosh Asadi +3
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes (…
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning, Extended version
Erwan Lecarpentier, Emmanuel Rachelson
This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Mode…
Open Loop Execution of Tree-Search Algorithms, extended version
Erwan Lecarpentier, Guillaume Infantes, Charles Lesire +1
In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an…
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring
Erwan Lecarpentier, Sebastian Rapp, Marc Melo +1
Autonomous unpowered flight is a challenge for control and guidance systems: all the energy the aircraft might use during flight has to be harvested directly from the atmosphere. W…