2.3k citations · 3.1k across the 16 of their papers we have counts for
4 papers · 1 filter
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto, David Meger, Doina Precup
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for…
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
Jason Gauci, Edoardo Conti, Yitao Liang +7
In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL probl…
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
Edward Smith, Scott Fujimoto, David Meger
We consider the problem of scaling deep generative shape models to high-resolution. Drawing motivation from the canonical view representation of objects, we introduce a novel metho…
Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto, Herke van Hoof, David Meger
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We…