M
Matteo Leonetti
Researcher at University of Leeds
Publications - 64
Citations - 1076
Matteo Leonetti is an academic researcher from University of Leeds. The author has contributed to research in topics: Reinforcement learning & Task (project management). The author has an hindex of 15, co-authored 56 publications receiving 688 citations. Previous affiliations of Matteo Leonetti include King's College London & Istituto Italiano di Tecnologia.
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Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
TL;DR: The authors presented a framework for curriculum learning in reinforcement learning, and used it to survey and classify existing curriculum learning methods in terms of their assumptions, capabilities, and goals, finding open problems and suggest directions for future RL curriculum learning research.
Journal ArticleDOI
BWIBots: A platform for bridging the gap between AI and human–robot interaction research:
Piyush Khandelwal,Shiqi Zhang,Shiqi Zhang,Jivko Sinapov,Matteo Leonetti,Matteo Leonetti,Jesse Thomason,Fangkai Yang,Ilaria Gori,Maxwell Svetlik,Priyanka Khante,Vladimir Lifschitz,Jake K. Aggarwal,Raymond J. Mooney,Peter Stone +14 more
TL;DR: A novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots designed as a part of the Building-Wide Intelligence project at the University of Texas at Austin is introduced.
Journal ArticleDOI
A synthesis of automated planning and reinforcement learning for efficient, robust decision-making
TL;DR: Domain Approximation for Reinforcement LearnING (DARLING) is presented, a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process.
Proceedings ArticleDOI
Source Task Creation for Curriculum Learning
TL;DR: This paper presents the more ambitious problem of curriculum learning in reinforcement learning, in which the goal is to design a sequence of source tasks for an agent to train on, such that final performance or learning speed is improved.
Proceedings Article
Automatic Curriculum Graph Generation for Reinforcement Learning Agents
TL;DR: This work introduces a method to generate a curriculum based on task descriptors and a novel metric of transfer potential that automatically generates a curriculum as a directed acyclic graph (as opposed to a linear sequence as done in existing work).