3.4k citations · 3.5k across the 11 of their papers we have counts for
12 papers
Understanding Self-Predictive Learning for Reinforcement Learning
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond +13
We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their…
Categorical SDEs with Simplex Diffusion
Pierre H. Richemond, Sander Dieleman, Arnaud Doucet
Diffusion models typically operate in the standard framework of generative modelling by producing continuously-valued datapoints. To this end, they rely on a progressive Gaussian s…
BYOL works even without batch statistics
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché +8
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a tar…
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché +11
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target…
Biologically inspired architectures for sample-efficient deep reinforcement learning
Pierre H. Richemond, Arinbjörn Kolbeinsson, Yike Guo
Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we u…
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli +2
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An altern…