9 citations · 36 across the 11 of their papers we have counts for
8 papers · 1 filter
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
Kei Ota, Devesh K. Jha, Diego Romeres +7
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success…
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota, Tomoaki Oiki, Devesh K. Jha +2
Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training lar…
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
Alberto Dalla Libera, Diego Romeres, Devesh K. Jha +2
In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR). In many mechanical syste…
Multi-label Prediction in Time Series Data using Deep Neural Networks
Wenyu Zhang, Devesh K. Jha, Emil Laftchiev +1
This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data. While fault (event) detection problems have been thoroughly studie…
Local Policy Optimization for Trajectory-Centric Reinforcement Learning
Patrik Kolaric, Devesh K. Jha, Arvind U. Raghunathan +4
The goal of this paper is to present a method for simultaneous trajectory and local stabilizing policy optimization to generate local policies for trajectory-centric model-based re…
Learning Dynamical Demand Response Model in Real-Time Pricing Program
Hanchen Xu, Hongbo Sun, Daniel Nikovski +2
Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functi…