5 citations · 15 across the 12 of their papers we have counts for
13 papers
Learning Abstract World Models with a Group-Structured Latent Space
Thomas Delliaux, Nguyen-Khanh Vu, Vincent François-Lavet +2
Learning meaningful abstract models of Markov Decision Processes (MDPs) is crucial for improving generalization from limited data. In this work, we show how geometric priors can be…
Solving robust MDPs as a sequence of static RL problems
Adil Zouitine, Matthieu Geist, Emmanuel Rachelson
Designing control policies whose performance level is guaranteed to remain above a given threshold in a span of environments is a critical feature for the adoption of reinforcement…
The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach
David Bertoin, Eduardo Hugo Sanchez, Mehdi Zouitine +1
Despite transformers being considered as the new standard in computer vision, convolutional neural networks (CNNs) still outperform them in low-data regimes. Nonetheless, CNNs ofte…
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…
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning
David Bertoin, Adil Zouitine, Mehdi Zouitine +1
Deep reinforcement learning policies, despite their outstanding efficiency in simulated visual control tasks, have shown disappointing ability to generalize across disturbances in…
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…
Large Batch Experience Replay
Thibault Lahire, Matthieu Geist, Emmanuel Rachelson
Several algorithms have been proposed to sample non-uniformly the replay buffer of deep Reinforcement Learning (RL) agents to speed-up learning, but very few theoretical foundation…
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…