This paper provides a literature review of some of the most important concepts, techniques,
and methodologies used within autonomous car systems. Specifically, we focus on two …

This paper analyzes a two-timescale stochastic algorithm framework for bilevel optimization.
Bilevel optimization is a class of problems which exhibits a two-level structure, and its goal is …

Actor-critic algorithm and their extensions have made great achievements in real-world
decision-making problems. In contrast to its empirical success, the theoretical understanding …

This literature review focuses on three important aspects of an autonomous car system:
tracking (assessing the identity of the actors such as cars, pedestrians or obstacles in a …

We study two time-scale linear stochastic approximation algorithms, which can be used to
model well-known reinforcement learning algorithms such as GTD, GTD2, and TDC. We …

Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in
recent years and has found many scientific and engineering applications. However, a key …

Risk management in dynamic decision problems is a primary concern in many fields,
including financial investment, autonomous driving, and healthcare. The mean-variance …

Off-policy learning is a key to extend reinforcement learning as it allows to learn a target
policy from a different behavior policy that generates the data. However, it is well known as …

Learning auxiliary tasks, such as multiple predictions about the world, can provide many
benefits to reinforcement learning systems. A variety of off-policy learning algorithms have …

Mobile robots are intelligent machines that can move and perform tasks in different
environments. The key factor enabling the autonomy of mobile robots lies in the reliability …