64 citations · 169 across the 29 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…
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
Juan Camilo Gamboa Higuera, David Meger, Gregory Dudek
We present an algorithm for rapidly learning controllers for robotics systems. The algorithm follows the model-based reinforcement learning paradigm, and improves upon existing alg…
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…