Transfer Learning for Multiagent Reinforcement Learning Systems by Felipe Leno Da Silva
Preface.- Acknowledgments.- Introduction.- Background.- Taxonomy.- Intra-Agent Transfer Methods.- Inter-Agent Transfer Methods.- Experiment Domains and Applications.- Current Challenges.- Resources.- Conclusion.- Bibliography.- Authors' Biographies .-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
Felipe Leno da Silva (Leno) holds a Ph.D. (2019) from the University of São Paulo, Brazil. He is currently a Postdoc Researcher at the Advanced Institute for AI, where he helped organe one of the first Brazilian AI residency programs. He has been actively researching knowledge reuse for multiagent RL since the start of his Ph.D. and is a firm believer that RL will bridge the gap between virtual agents and the physical real world. Leno enjoys serving the AI community in oft-neglected yet important roles. He has been part of the Program Committees of most of the major AI conferences and has organized multiple workshops, such as the Adaptive and Learning Agents (ALA) and the Scaling-Up Reinforcement Learning (SURL) workshop series. Leno is a strong advocate for the inclusion of minorities in the AI community and has been involved in multiple iterations of the Latinx in AI workshop at NeurIPS.Anna Helena Reali Costa (Anna Reali) is Full Professor at Universidade de Sã o Paulo (USP), Brazil. She received her Ph.D. at USP, investigated robot vision as a research scientist at the University of Karlsruhe, and was a guest researcher at Carnegie Mellon University, working in the integration of learning, planning, and execution in mobile robot teams. She is the Director of the Data Science Center (C2D), a partnership between USP and the Itau-Unibanco bank, and a member of the Center for Artificial Intelligence (C4AI), a partnership between USP, IBM, and FAPESP. Her scientific contributions lie in AI and Machine Learning, in particular RL; her long-term research objective is to create autonomous, ethical, and robust agents that can learn to interact in complex and dynamic environments, aiming at the well-being of human beings.
| SKU | Unavailable |
| ISBN 13 | 9783031004636 |
| ISBN 10 | 3031004639 |
| Title | Transfer Learning for Multiagent Reinforcement Learning Systems |
| Author | Felipe Leno Da Silva |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2021-05-27 |
| Number of pages | 111 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |