34 citations · 53 across the 22 of their papers we have counts for
31 papers
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Jiachen Li, David Isele, Kanghoon Lee +3
Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural netwo…
A POMDP Model for Safe Geological Carbon Sequestration
Anthony Corso, Yizheng Wang, Markus Zechner +2
Geological carbon capture and sequestration (CCS), where CO is stored in subsurface formations, is a promising and scalable approach for reducing global emissions. However, if…
A Deep Reinforcement Learning Approach to Rare Event Estimation
Anthony Corso, Kyu-Young Kim, Shubh Gupta +2
An important step in the design of autonomous systems is to evaluate the probability that a failure will occur. In safety-critical domains, the failure probability is extremely sma…
Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction
Masha Itkina, Mykel J. Kochenderfer
Although neural networks have seen tremendous success as predictive models in a variety of domains, they can be overly confident in their predictions on out-of-distribution (OOD) d…
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning
Jennifer She, Jayesh K. Gupta, Mykel J. Kochenderfer
Sparse and delayed rewards pose a challenge to single agent reinforcement learning. This challenge is amplified in multi-agent reinforcement learning (MARL) where credit assignment…
Multi-Objective Policy Gradients with Topological Constraints
Kyle Hollins Wray, Stas Tiomkin, Mykel J. Kochenderfer +1
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a c…