6 citations · 11 across the 2 of their papers we have counts for
7 papers
Launchpad: A Programming Model for Distributed Machine Learning Research
Fan Yang, Gabriel Barth-Maron, Piotr Stańczyk +5
A major driver behind the success of modern machine learning algorithms has been their ability to process ever-larger amounts of data. As a result, the use of distributed systems i…
Reverb: A Framework For Experience Replay
Albin Cassirer, Gabriel Barth-Maron, Eugene Brevdo +4
A central component of training in Reinforcement Learning (RL) is Experience: the data used for training. The mechanisms used to generate and consume this data have an important ef…
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Tom Le Paine, Caglar Gulcehre, Bobak Shahriari +11
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial…
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang +8
Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap…
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…