7 citations · 13 across the 11 of their papers we have counts for
12 papers
The Open Ant: A Robot Platform for Reinforcement Learning Research
Elena Sorina Lupu, Patrick Spieler, Khurram Javed +4
Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations. The predom…
Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment
Xi Lin, Paul Szenher, John D. Martin +1
Route planning is essential to mobile robot navigation problems. In recent years, deep reinforcement learning (DRL) has been applied to learning optimal planning policies in stocha…
Settling the Reward Hypothesis
Michael Bowling, John D. Martin, David Abel +1
The reward hypothesis posits that, "all of what we mean by goals and purposes can be well thought of as maximization of the expected value of the cumulative sum of a received scala…
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…
Stochastically Dominant Distributional Reinforcement Learning
John D. Martin, Michal Lyskawinski, Xiaohu Li +1
We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose…
Recursive Sparse Pseudo-input Gaussian Process SARSA
John Martin, Brendan Englot
The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning. In this paper, we consider a rec…
Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation
John Martin, Jinkun Wang, Brendan Englot
We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficien…