84 citations · 339 across the 34 of their papers we have counts for
23 papers · 1 filter
Safe Reinforcement Learning Using Advantage-Based Intervention
Nolan Wagener, Byron Boots, Ching-An Cheng
Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the developmen…
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning
Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots
Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For each state encountere…
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Siddarth Srinivasan, Sandesh Adhikary, Jacob Miller +2
Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix product states, a tensor-train decomposition…
Explaining Fast Improvement in Online Imitation Learning
Xinyan Yan, Byron Boots, Ching-An Cheng
Online imitation learning (IL) is an algorithmic framework that leverages interactions with expert policies for efficient policy optimization. Here policies are optimized by perfor…
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Amir Rahimi, Amirreza Shaban, Ching-An Cheng +2
Predicting calibrated confidence scores for multi-class deep networks is important for avoiding rare but costly mistakes. A common approach is to learn a post-hoc calibration funct…
Information Theoretic Model Predictive Q-Learning
Mohak Bhardwaj, Ankur Handa, Dieter Fox +1
Model-free Reinforcement Learning (RL) works well when experience can be collected cheaply and model-based RL is effective when system dynamics can be modeled accurately. However,…