research
∙
05/30/2023
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves su...
share
research
∙
06/03/2022
Beyond Tabula Rasa: Reincarnating Reinforcement Learning
Learning tabula rasa, that is without any prior knowledge, is the preval...
research
∙
05/16/2022
The Primacy Bias in Deep Reinforcement Learning
This work identifies a common flaw of deep reinforcement learning (RL) a...
research
∙
04/01/2022
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
We introduce Simplicial Embeddings (SEMs) as a way to constrain the enco...
research
∙
08/30/2021
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated ...
research
∙
06/09/2021
Pretraining Representations for Data-Efficient Reinforcement Learning
Data efficiency is a key challenge for deep reinforcement learning. We a...
research
∙
05/03/2021
Iterated learning for emergent systematicity in VQA
Although neural module networks have an architectural bias towards compo...
research
∙
07/12/2020
Data-Efficient Reinforcement Learning with Momentum Predictive Representations
While deep reinforcement learning excels at solving tasks where large am...
research
∙
06/19/2019
GEAR: Geometry-Aware Rényi Information
Shannon's seminal theory of information has been of paramount importance...
research
∙
10/14/2018
