104 citations · 374 across the 40 of their papers we have counts for
10 papers · 1 filter
Scalable Reinforcement Learning Policies for Multi-Agent Control
Christopher D. Hsu, Heejin Jeong, George J. Pappas +1
We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary number of pursuers and targ…
An Information-Geometric Distance on the Space of Tasks
Yansong Gao, Pratik Chaudhari
This paper prescribes a distance between learning tasks modeled as joint distributions on data and labels. Using tools in information geometry, the distance is defined to be the le…
Proximal Deterministic Policy Gradient
Marco Maggipinto, Gian Antonio Susto, Pratik Chaudhari
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms. First, we formulate off-policy RL as a stochastic proximal point iteration…
MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation
Xiaoyi Chen, Pratik Chaudhari
Autonomous navigation in crowded, complex urban environments requires interacting with other agents on the road. A common solution to this problem is to use a prediction model to g…
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning
Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola
This paper prescribes a suite of techniques for off-policy Reinforcement Learning (RL) that simplify the training process and reduce the sample complexity. First, we show that simp…
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Rasool Fakoor, Jonas Mueller, Nick Erickson +2
Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor es…