33 citations · 38 across the 7 of their papers we have counts for
5 papers · 1 filter
Unbiased Methods for Multi-Goal Reinforcement Learning
Léonard Blier, Yann Ollivier
In multi-goal reinforcement learning (RL) settings, the reward for each goal is sparse, and located in a small neighborhood of the goal. In large dimension, the probability of reac…
Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint
Léonard Blier, Corentin Tallec, Yann Ollivier
In reinforcement learning, temporal difference-based algorithms can be sample-inefficient: for instance, with sparse rewards, no learning occurs until a reward is observed. This ca…
Making Deep Q-learning methods robust to time discretization
Corentin Tallec, Léonard Blier, Yann Ollivier
Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017,…
Learning with Random Learning Rates
Léonard Blier, Pierre Wolinski, Yann Ollivier
Hyperparameter tuning is a bothersome step in the training of deep learning models. One of the most sensitive hyperparameters is the learning rate of the gradient descent. We prese…
The Description Length of Deep Learning Models
Léonard Blier, Yann Ollivier
Solomonoff's general theory of inference and the Minimum Description Length principle formalize Occam's razor, and hold that a good model of data is a model that is good at lossles…