1.6k citations · 2.7k across the 33 of their papers we have counts for
12 papers · 1 filter
Block Contextual MDPs for Continual Learning
Shagun Sodhani, Franziska Meier, Joelle Pineau +1
In reinforcement learning (RL), when defining a Markov Decision Process (MDP), the environment dynamics is implicitly assumed to be stationary. This assumption of stationarity, whi…
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Lucas Caccia, Joelle Pineau
This paper presents SPeCiaL: a method for unsupervised pretraining of representations tailored for continual learning. Our approach devises a meta-learning objective that different…
Correcting Momentum in Temporal Difference Learning
Emmanuel Bengio, Joelle Pineau, Doina Precup
A common optimization tool used in deep reinforcement learning is momentum, which consists in accumulating and discounting past gradients, reapplying them at each iteration. We arg…
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs
Harsh Satija, Philip S. Thomas, Joelle Pineau +1
We study the problem of Safe Policy Improvement (SPI) under constraints in the offline Reinforcement Learning (RL) setting. We consider the scenario where: (i) we have a dataset co…
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi +3
In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small sub…
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
Kalesha Bullard, Douwe Kiela, Franziska Meier +2
Effective communication is an important skill for enabling information exchange in multi-agent settings and emergent communication is now a vibrant field of research, with common s…