1.1k citations · 1.1k across the 8 of their papers we have counts for
8 papers · 1 filter
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz +4
We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost cons…
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…
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
Peter Karkus, Mehdi Mirza, Arthur Guez +5
Intelligent robots need to achieve abstract objectives using concrete, spatiotemporally complex sensory information and motor control. Tabula rasa deep reinforcement learning (RL)…
Value-driven Hindsight Modelling
Arthur Guez, Fabio Viola, Théophane Weber +5
Value estimation is a critical component of the reinforcement learning (RL) paradigm. The question of how to effectively learn value predictors from data is one of the major proble…
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert +9
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge suc…
Augmenting learning using symmetry in a biologically-inspired domain
Shruti Mishra, Abbas Abdolmaleki, Arthur Guez +2
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimension…