Learning, planning, and representing knowledge at multiple levels of temporal abstraction
are key, longstanding challenges for AI. In this paper we consider how these challenges can …

Autonomously discovering temporally extended actions, or skills, is a longstanding goal of
hierarchical reinforcement learning. We propose a new algorithm that combines skill …

By using smart radio devices, a jammer can dynamically change its jamming policy based
on opposing security mechanisms; it can even induce the mobile device to enter a specific …

In the field of Sequential Decision Making (SDM), two paradigms have historically vied for
supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of …

Identifying statistical regularities in solutions to some tasks in multi-task reinforcement
learning can accelerate the learning of new tasks. Skill learning offers one way of identifying …

Trajectory planning and optimization is a fundamental problem in articulated robotics.
Algorithms used typically for this problem compute optimal trajectories from scratch in a new …

Learning, planning, and representing knowledge at multiple levels of temporal abstraction
are key challenges for AI. In this paper we develop an approach to these problems based on …

Recent works on shared autonomy and assistive-AI technologies, such as assistive robotic
teleoperation, seek to model and help human users with limited ability in a fixed task …

We study model-based reinforcement learning in an unknown finite communicating Markov
decision process. We propose a simple algorithm that leverages a variance based …

This paper presents a number of approaches for solving a real-time game consisting of a
ship that must visit a number of waypoints scattered around a 2-D maze full of obstacles. The …