1.3k citations · 4.8k across the 58 of their papers we have counts for
8 papers · 1 filter
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer, Jesse Farebrother, Joshua Greaves +8
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning…
Small batch deep reinforcement learning
Johan Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
In value-based deep reinforcement learning with replay memories, the batch size parameter specifies how many transitions to sample for each gradient update. Although critical to th…
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
Nate Rahn, Pierluca D'Oro, Harley Wiltzer +2
Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In this work, we provide a fresh perspective…
Bootstrapped Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Mark Rowland +4
In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of deep learning algorithms is to automati…
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Mark Rowland, Yunhao Tang, Clare Lyle +3
We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distributional reinforcement learning algorith…
Bigger, Better, Faster: Human-level Atari with human-level efficiency
Max Schwarzer, Johan Obando-Ceron, Aaron Courville +3
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value…
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal +4
Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well understood; in practice, however, their pri…
An Analysis of Quantile Temporal-Difference Learning
Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar +6
We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale ap…