P
Pablo Hernandez-Leal
Researcher at Centrum Wiskunde & Informatica
Publications - 62
Citations - 1601
Pablo Hernandez-Leal is an academic researcher from Centrum Wiskunde & Informatica. The author has contributed to research in topics: Reinforcement learning & Repeated game. The author has an hindex of 17, co-authored 62 publications receiving 1097 citations. Previous affiliations of Pablo Hernandez-Leal include National Institute of Astrophysics, Optics and Electronics.
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A survey and critique of multiagent deep reinforcement learning
TL;DR: In this paper, the authors provide a clear overview of current multi-agent deep reinforcement learning (MDRL) literature, and provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research.
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Local Energy Markets: Paving the Path Toward Fully Transactive Energy Systems
TL;DR: In this paper, the authors evaluate a fully integrated transactive system by modeling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation, and coupling these elements into an integrated trans-active energy simulation.
Posted Content
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
TL;DR: This survey presents a coherent overview of work that addresses opponent-induced non-stationarity with tools from game theory, reinforcement learning and multi-armed bandits, arriving at a new framework and five categories (in increasing order of sophistication): ignore, forget, respond to target models, learn models, and theory of mind.
Posted Content
Is multiagent deep reinforcement learning the answer or the question? A brief survey
TL;DR: This article provides a clear overview of current multiagent deep reinforcement learning (MDRL) literature and provides guidelines to complement this emerging area by showcasing examples on how methods and algorithms from DRL and multiagent learning (MAL) have helped solve problems in MDRL and providing general lessons learned from these works.
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Multi-label classification with Bayesian network-based chain classifiers
L. Enrique Sucar,Concha Bielza,Eduardo F. Morales,Pablo Hernandez-Leal,Julio H. Zaragoza,Pedro Larrañaga +5 more
TL;DR: It is shown that a random chain order considering the constraints imposed by a Bayesian network with a simple tree-based structure can have very competitive results in terms of predictive performance and time complexity against related state-of-the-art approaches.