487 citations · 1.6k across the 7 of their papers we have counts for
4 papers · 1 filter
Observe and Look Further: Achieving Consistent Performance on Atari
Tobias Pohlen, Bilal Piot, Todd Hester +10
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks…
Distributed Distributional Deterministic Policy Gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden +6
This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed fram…
Distributed Prioritized Experience Replay
Dan Horgan, John Quan, David Budden +4
We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible…
Unicorn: Continual Learning with a Universal, Off-policy Agent
Daniel J. Mankowitz, Augustin Žídek, André Barreto +7
Some real-world domains are best characterized as a single task, but for others this perspective is limiting. Instead, some tasks continually grow in complexity, in tandem with the…