87 citations · 243 across the 11 of their papers we have counts for
11 papers
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…
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…
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro +2
Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale setti…
The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro +2
This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of…
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
Samuel Lavoie, Christos Tsirigotis, Max Schwarzer +4
Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into simplices of dimensions each usin…
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro +2
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. Most published results on deep RL benchma…