588 citations
- Google DeepMind (United Kingdom)GB18 papers
- Google (United States)US10 papers
- Stanford UniversityUS3 papers
- Carnegie Mellon UniversityUS2 papers
- ETH ZurichCH2 papers
- Google (Switzerland)CH2 papers
- King's College LondonGB2 papers
- Massachusetts Institute of TechnologyUS2 papers
- University College LondonGB2 papers
- University of California San DiegoUS2 papers
- University of GlasgowGB2 papers
- University of TorontoCA2 papers
5 papers · 1 filter
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…
LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia
Steven M. Goodman, Erin Buehler, Patrick Clary +15
Prior work has explored the writing challenges experienced by people with dyslexia, and the potential for new spelling, grammar, and word retrieval technologies to address these ch…
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Filip Radlinski, Krisztian Balog, Fernando Diaz +2
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people's understa…
Marginalized Operators for Off-policy Reinforcement Learning
Yunhao Tang, Mark Rowland, Rémi Munos +1
In this work, we propose marginalized operators, a new class of off-policy evaluation operators for reinforcement learning. Marginalized operators strictly generalize generic multi…
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal, Abram L. Friesen, Andrea Banino +13
Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has se…