3.4k citations · 4.9k across the 135 of their papers we have counts for
13 papers · 1 filter
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Ran Liu, Mehdi Azabou, Max Dabagia +5
Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. However, without labels, f…
Adaptive Multi-Goal Exploration
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard +3
We introduce a generic strategy for provably efficient multi-goal exploration. It relies on AdaGoal, a novel goal selection scheme that leverages a measure of uncertainty in reachi…
Taylor Expansion of Discount Factors
Yunhao Tang, Mark Rowland, Rémi Munos +1
In practical reinforcement learning (RL), the discount factor used for estimating value functions often differs from that used for defining the evaluation objective. In this work,…
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
Tadashi Kozuno, Pierre Ménard, Rémi Munos +1
We study the problem of learning a Nash equilibrium (NE) in an imperfect information game (IIG) through self-play. Precisely, we focus on two-player, zero-sum, episodic, tabular II…
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
Yunhao Tang, Tadashi Kozuno, Mark Rowland +2
Model-agnostic meta-reinforcement learning requires estimating the Hessian matrix of value functions. This is challenging from an implementation perspective, as repeatedly differen…
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
Jean Tarbouriech, Runlong Zhou, Simon S. Du +3
We study the problem of learning in the stochastic shortest path (SSP) setting, where an agent seeks to minimize the expected cost accumulated before reaching a goal state. We desi…
Broaden Your Views for Self-Supervised Video Learning
Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac +11
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by…
UCB Momentum Q-learning: Correcting the bias without forgetting
Pierre Menard, Omar Darwiche Domingues, Xuedong Shang +1
We propose UCBMQ, Upper Confidence Bound Momentum Q-learning, a new algorithm for reinforcement learning in tabular and possibly stage-dependent, episodic Markov decision process.…
Revisiting Peng's Q() for Modern Reinforcement Learning
Tadashi Kozuno, Yunhao Tang, Mark Rowland +5
Off-policy multi-step reinforcement learning algorithms consist of conservative and non-conservative algorithms: the former actively cut traces, whereas the latter do not. Recently…
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu +10
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample. Without sufficient…
Large-Scale Representation Learning on Graphs via Bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar +5
Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs. However, to achieve state-of-the-…
Geometric Entropic Exploration
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade +7
Exploration is essential for solving complex Reinforcement Learning (RL) tasks. Maximum State-Visitation Entropy (MSVE) formulates the exploration problem as a well-defined policy…
On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions
Pierre Perrault, Jennifer Healey, Zheng Wen +1
We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another algorithm having a different…