京东 11.11 红包
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent..
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The Provable Effectiveness of Policy Gradient Methods in Reinforcement Learning.
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Safe and Efficient Exploration in Reinforcement Learning by Andreas Krause
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Learning-based Koopman Modeling for Efficient State Estimation and Control ...
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Data-driven MPC: From Linear to Nonlinear Systems with Guarantees by F. Allgöwer
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Data-Driven Dynamical Systems and Control by Steve Brunton
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Driving the Elephant through the Landscape of Deep Networks Stefano Soatto
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Modeling for Multi-Agent Collaboration by Monroe Kennedy III
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Leveraging Learning & Opt.-based Planning for Multi-Robot Systems by P. Tokekar
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Learning for Decision-Making under Uncertainty by Bartolomeo Stellato
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Safe, Interaction-Aware Decision Making and Control for Robot Autonomy by Pavone
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A Constrained Control Perspective on Safe Learning-Based Control by M. Zeilinger
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The Long and Short of Stochastic Stability in Multi-Agent Systems by Jeff Shamma
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"Industrial Potential of Reinforcement Learning" Panel Discussion @ IFAC2020
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IFAC Industry Connect: Machine Learning and Control
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Safe Exploration in Reinforcement Learning by Andreas Krause (ETH Zuerich)
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Equivariant Observers: Robust Nonlinear State Estimation for Robotic Systems
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Multi-fidelity Gaussian Processes for Human-Agent Teaming by Vaibhav Srivastava
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On the Structure of Learning: What’s in the Black Box? by Patricio Antonio Vela
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Fast and Flexible Multi-Agent Decision-Making by Naomi Leonard @ CDC 2023
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Real-time Distributed Decision Making in Networked Systems by Na Li
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The Role of Adaptation in Learning, Safety, and Optimality by Anuradha Annaswamy
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Robot and Remote-controlled Machine Technology for Accident Response and ...
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Distributed Machine Learning over Networks by Francis Bach @CDC2019
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Regret-Optimal Control by Babak Hassibi
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Robots Interacting with Humans and Unknown Worlds by Alin Albu-Schaeffer
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Data Auctions with Externalities by Munther Dahleh
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Control Has Met Learning: Aspirational Lessons from Adaptive Control Theory
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The Non-Stochastic Control Problem by Elad Hazan
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IFAC Industry Connect : The Impact of Control Research on Industrial Innovation
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Towards Safe & Resilient Autonomy using Synergistic Control, Observation and ...
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Contraction Analysis in Optimization, Learning, and Adaptive Prediction &Control
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Why Would We Want a Multi-agent System Unstable? by Mrdjan Jankovic @ACC2023
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Gradient based Methods for Control and Learning of Dynamical Systems by M. Fazel
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Learning Certifiably Safe Control for Large-scale Autonomous Systems by C Fan
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Reinforcement Learning for Process Control and Beyond by Jay H. Lee
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Genetic Circuit Engineering Meets Control Theory by Domitilla Del Vecchio
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Physics-Guided Data-Driven Modeling for Control in Additive Manufacturing
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Direct Adaptive Learning of the LQR by Florian Dörfler
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Data-driven Input-to-state Stabilization by Andrea Bisoffi
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Regulation-Triggered Batch Learning: A New Hope For Applied Adaptive Control