1 citations · 1 across the 4 of their papers we have counts for
9 papers
Should Models Be Accurate?
Esra'a Saleh, John D. Martin, Anna Koop +2
Model-based Reinforcement Learning (MBRL) holds promise for data-efficiency by planning with model-generated experience in addition to learning with experience from the environment…
Adapting the Function Approximation Architecture in Online Reinforcement Learning
John D. Martin, Joseph Modayil
The performance of a reinforcement learning (RL) system depends on the computational architecture used to approximate a value function. Deep learning methods provide both optimizat…
Variational Filtering with Copula Models for SLAM
John D. Martin, Kevin Doherty, Caralyn Cyr +2
The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled…
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
Fanfei Chen, John D. Martin, Yewei Huang +2
We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently i…
Fusing Concurrent Orthogonal Wide-aperture Sonar Images for Dense Underwater 3D Reconstruction
John McConnell, John D. Martin, Brendan Englot
We propose a novel approach to handling the ambiguity in elevation angle associated with the observations of a forward looking multi-beam imaging sonar, and the challenges it poses…
On Catastrophic Interference in Atari 2600 Games
William Fedus, Dibya Ghosh, John D. Martin +3
Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interference within an environment inhibits le…