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Training sophisticated agents for optimal decision-making under uncertainty has been key to the rapid development of modern autonomous systems across fields.
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A survey of actor-critic reinforcement learning: Standard and natural policy gradients
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Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
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Playing atari with deep reinforcement learning, 2013
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Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning
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Trust Region Policy Optimization
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Concrete problems in AI safety, 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Prioritized Experience Replay, 2016
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
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Bayesian optimization in a billion dimensions via random embeddings
Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, and Nando De Freitas · 2016
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Deep kernel learning
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Hindsight Experience Replay
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A distributional perspective on reinforcement learning
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Learning robust rewards with adversarial inverse reinforcement learning, 2017
Justin Fu, Katie Luo, and Sergey Levine · 2017
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Preferential Bayesian optimization
Javier González, Zhenwen Dai, Andreas Damianou, and Neil D. Lawrence · 2017
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Model Predictive Control: Theory, Computation, and Design
James Blake Rawlings, David Q Mayne, and Moritz Diehl · 2017
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Trust region policy optimization, 2017
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, and Pieter Abbeel · 2017
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Proximal policy optimization algorithms, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Generalization guarantees for imitation learning
Allen Ren, Sushant Veer, and Anirudha Majumdar · 2021
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A data-driven automatic tuning method for MPC under uncertainty using constrained Bayesian optimization
Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah, and Joel A Paulson · 2021
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Safe Reinforcement Learning Using Robust MPC
Mario Zanon and Sebastien Gros · 2021
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Preference-based MPC calibration
Mengjia Zhu, Alberto Bemporad, and Dario Piga · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai et al · 2022
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A survey on high-dimensional gaussian process modeling with application to Bayesian optimization
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Off-policy evaluation for slate recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, and Imed Zitouni · 2017
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Differentiable MPC for End-to-end Planning and Control
Brandon Amos, Ivan Jimenez, Jacob Sacks, Byron Boots, and J. Zico Kolter · 2018
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Model-Based Reinforcement Learning via Meta-Policy Optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, and Pieter Abbeel · 2018
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Global convergence of policy gradient methods for the linear quadratic regulator
Maryam Fazel, Rong Ge, Sham Kakade, and Mehran Mesbahi · 2018
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A tutorial on Bayesian optimization, 2018
Peter I. Frazier · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Data-efficient reinforcement learning with probabilistic model predictive control
Sanket Kamthe and Marc Deisenroth · 2018
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Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
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On controller tuning with time-varying bayesian optimization
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Multi-objective Bayesian optimization over high-dimensional search spaces
Samuel Daulton, David Eriksson, Maximilian Balandat, and Eytan Bakshy · 2022
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Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
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Learning for MPC with stability & safety guarantees
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No-Regret Bayesian Optimization with Unknown Equality and Inequality Constraints using Exact Penalty Functions
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