Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

In recent years, autonomous vehicles (AVs), which observe the driving environment and
lead a few or all of the driving tasks, have garnered tremendous success. The field of AVs …

A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …

Tactical decision making for autonomous driving is challenging due to the diversity of
environments, the uncertainty in the sensor information, and the complex interaction with …

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the
precision of tree search with the generality of random sampling. It has received considerable …

Online solvers for partially observable Markov decision processes have been applied to
problems with large discrete state spaces, but continuous state, action, and observation …

This paper presents a method for testing the decision making systems of autonomous
vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment …

This paper presents a method for constructing human-robot interaction policies in settings
where multimodality, ie, the possibility of multiple highly distinct futures, plays a critical role …

Decision making in dense traffic can be challenging for autonomous vehicles. An
autonomous system only relying on predefined road priorities and considering other drivers …

Reinforcement learning (RL) approaches that combine a tree search with deep learning
have found remarkable success in searching exorbitantly large, albeit discrete action …