6 citations · 17 across the 8 of their papers we have counts for
9 papers
Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains
Junhong Xu, Kai Yin, Zheng Chen +3
We propose a diffusion approximation method to the continuous-state Markov Decision Processes (MDPs) that can be utilized to address autonomous navigation and control in unstructur…
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts
Amanda Prorok, Jan Blumenkamp, Qingbiao Li +3
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of le…
Composable Learning with Sparse Kernel Representations
Ekaterina Tolstaya, Ethan Stump, Alec Koppel +1
We present a reinforcement learning algorithm for learning sparse non-parametric controllers in a Reproducing Kernel Hilbert Space. We improve the sample complexity of this approac…
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
Nicholas Waytowich, Sean L. Barton, Vernon Lawhern +2
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniq…
Intelligent Autonomous Things on the Battlefield
Alexander Kott, Ethan Stump
Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in…
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems
Alec Koppel, Ekaterina Tolstaya, Ethan Stump +1
We consider Markov Decision Problems defined over continuous state and action spaces, where an autonomous agent seeks to learn a map from its states to actions so as to maximize it…