138 citations · 402 across the 159 of their papers we have counts for
23 papers · 1 filter
Online Planning for Constrained POMDPs with Continuous Spaces through Dual Ascent
Arec Jamgochian, Anthony Corso, Mykel J. Kochenderfer
Rather than augmenting rewards with penalties for undesired behavior, Constrained Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing inviolable hard c…
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…
Optimality Guarantees for Particle Belief Approximation of POMDPs
Michael H. Lim, Tyler J. Becker, Mykel J. Kochenderfer +2
Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to…
LOPR: Latent Occupancy PRediction using Generative Models
Bernard Lange, Masha Itkina, Mykel J. Kochenderfer
Environment prediction frameworks are integral for autonomous vehicles, enabling safe navigation in dynamic environments. LiDAR generated occupancy grid maps (L-OGMs) offer a robus…
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…
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction
Jiachen Li, Chuanbo Hua, Jinkyoo Park +3
While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is…
Interaction Modeling with Multiplex Attention
Fan-Yun Sun, Isaac Kauvar, Ruohan Zhang +4
Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that…
Strategic Asset Allocation with Illiquid Alternatives
Eric Luxenberg, Stephen Boyd, Mykel Kochenderfer +6
We address the problem of strategic asset allocation (SAA) with portfolios that include illiquid alternative asset classes. The main challenge in portfolio construction with illiqu…
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Bertrand Charpentier, Ransalu Senanayake, Mykel Kochenderfer +1
Characterizing aleatoric and epistemic uncertainty on the predicted rewards can help in building reliable reinforcement learning (RL) systems. Aleatoric uncertainty results from th…
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction
Junyoung Park, Federico Berto, Arec Jamgochian +2
In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by…
SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
Arec Jamgochian, Etienne Buehrle, Johannes Fischer +1
Designing a safe and human-like decision-making system for an autonomous vehicle is a challenging task. Generative imitation learning is one possible approach for automating policy…
How Do We Fail? Stress Testing Perception in Autonomous Vehicles
Harrison Delecki, Masha Itkina, Bernard Lange +2
Autonomous vehicles (AVs) rely on environment perception and behavior prediction to reason about agents in their surroundings. These perception systems must be robust to adverse we…
FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget
Oriana Peltzer, Amanda Bouman, Sung-Kyun Kim +8
We present a method for autonomous exploration of large-scale unknown environments under mission time constraints. We start by proposing the Frontloaded Information Gain Orienteeri…
A Mixed Integer Programming Approach for Verifying Properties of Binarized Neural Networks
Christopher Lazarus, Mykel J. Kochenderfer
Many approaches for verifying input-output properties of neural networks have been proposed recently. However, existing algorithms do not scale well to large networks. Recent work…
Deep Binary Reinforcement Learning for Scalable Verification
Christopher Lazarus, Mykel J. Kochenderfer
The use of neural networks as function approximators has enabled many advances in reinforcement learning (RL). The generalization power of neural networks combined with advances in…
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Xiaobai Ma, David Isele, Jayesh K. Gupta +2
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requ…
Model Predictive Optimized Path Integral Strategies
Dylan M. Asmar, Ransalu Senanayake, Shawn Manuel +1
We generalize the derivation of model predictive path integral control (MPPI) to allow for a single joint distribution across controls in the control sequence. This reformation all…
A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer
Gabriel Maher, Stephen Boyd, Mykel Kochenderfer +5
We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multipl…
Verifying Inverse Model Neural Networks
Chelsea Sidrane, Sydney Katz, Anthony Corso +1
Inverse problems exist in a wide variety of physical domains from aerospace engineering to medical imaging. The goal is to infer the underlying state from a set of observations. Wh…
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand
Arec Jamgochian, Di Wu, Kunal Menda +2
Some real-world decision-making problems require making probabilistic forecasts over multiple steps at once. However, methods for probabilistic forecasting may fail to capture corr…