424 citations · 1.3k across the 16 of their papers we have counts for
4 papers · 1 filter
Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal +4
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic…
Deep Reinforcement Learning and its Neuroscientific Implications
Matthew Botvinick, Jane X. Wang, Will Dabney +2
The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained us…
Temporally-Extended ε-Greedy Exploration
Will Dabney, Georg Ostrovski, André Barreto
Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expen…
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning
Will Dabney, André Barreto, Mark Rowland +4
In value-based reinforcement learning (RL), unlike in supervised learning, the agent faces not a single, stationary, approximation problem, but a sequence of value prediction probl…