Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents | Paper Citation Record

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This paper cites Preference-Based Policy Learning.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Preference-Based Policy Learning

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This paper cites Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, and Paulo Tabuada.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, and Paulo Tabuada

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This paper cites Concrete Problems in AI Safety.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Concrete Problems in AI Safety

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Zico Kolter

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This paper cites A Painless Deterministic Policy Gradient Method for Learning-based MPC.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents A Painless Deterministic Policy Gradient Method for Learning-based MPC

Reference 5

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This paper cites Andrychowicz et al.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Andrychowicz et al

Reference 6

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Unresolved cited work

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This paper cites MPC-based reinforcement learning for economic problems with application to battery storage.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents MPC-based reinforcement learning for economic problems with application to battery storage

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This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 9

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This paper cites Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learning.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learning

Reference 10

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This paper cites Gradient-Based Framework for Bilevel Optimization of Black-Box Functions: Synergizing Model-Free Reinforcement Learning and Implicit Function Differentiation.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Gradient-Based Framework for Bilevel Optimization of Black-Box Functions: Synergizing Model-Free Reinforcement Learning and Implicit Function Differentiation

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This paper cites Bellemare, Will Dabney, and R´ emi Munos.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Bellemare, Will Dabney, and R´ emi Munos

Reference 12

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This paper cites Bellman and R.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Bellman and R

Reference 13

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Unresolved cited work

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This paper cites Bellman and Stuart E.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Bellman and Stuart E

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Schoellig

Reference 16

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This paper cites Bertsekas and J.N.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Bertsekas and J.N

Reference 17

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This paper cites Global Optimality Guarantees For Policy Gradient Methods.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Global Optimality Guarantees For Policy Gradient Methods

Reference 18

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This paper cites Differentiable optimization-based control policy with convergence analysis, 2025.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Differentiable optimization-based control policy with convergence analysis, 2025

Reference 19

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This paper cites A survey on high- dimensional gaussian process modeling with application to Bayesian optimization.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents A survey on high- dimensional gaussian process modeling with application to Bayesian optimization

Reference 20

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This paper cites Blondel and John N.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Blondel and John N

Reference 21

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This paper cites Time-varying gaussian process bandit optimization.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Time-varying gaussian process bandit optimization

Reference 22

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This paper cites Optimization of the model predictive control meta-parameters through reinforcement learning.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Optimization of the model predictive control meta-parameters through reinforcement learning

Reference 23

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This paper cites Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

Reference 24

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This paper cites On controller tuning with time-varying bayesian optimization.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents On controller tuning with time-varying bayesian optimization

Reference 25

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This paper cites Reinforcement Learning of the Prediction Horizon in Model Predictive Control.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Reinforcement Learning of the Prediction Horizon in Model Predictive Control

Reference 26

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This paper cites MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles

Reference 27

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This paper cites Chan, Georgios Makrygiorgos, and Ali Mesbah.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Chan, Georgios Makrygiorgos, and Ali Mesbah

Reference 28

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Chan, Joel A

Reference 29

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This paper cites Chan, Joel A.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Chan, Joel A

Reference 30

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Goal- conditioned reinforcement learning with imagined subgoals

Reference 31

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This paper cites Gnu-RL: A precocial reinforcement learning solution for building hvac control using a differentiable MPC policy.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Gnu-RL: A precocial reinforcement learning solution for building hvac control using a differentiable MPC policy

Reference 32

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Intrinsically motivated reinforcement learning

Reference 33

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Run- indexed time-varying Bayesian optimization with positional encoding for auto-tuning of controllers: Application to a plasma-assisted deposition process with run-to-run drifts

Reference 34

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Choksi and Joel A

Reference 35

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Deep reinforcement learning from human preferences

Reference 36

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Model-Based Reinforcement Learning via Meta-Policy Optimization

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Bayesian reinforcement learning in continuous POMDPs with gaussian processes

Reference 39

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Unexpected improvements to expected improvement for Bayesian optimization

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Multi-objective Bayesian optimization over high-dimensional search spaces

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Osborne, and Eytan Bakshy

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Mixed-Variable Bayesian Optimization

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Gymnasium robotics, 2024

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Unresolved cited work

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Dontchev and R

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Additive Gaussian Processes

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Infinite-Horizon Differentiable Model Predictive Control

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents High-dimensional Bayesian optimization with sparse axis-aligned subspaces

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Scalable global optimization via local Bayesian optimization

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC

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This paper cites Global convergence of policy gradient methods for the linear quadratic regulator.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Global convergence of policy gradient methods for the linear quadratic regulator

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This paper cites Dynamic Regret of Policy Optimization in Non- Stationary Environments.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Dynamic Regret of Policy Optimization in Non- Stationary Environments

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Unresolved cited work

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents A Tutorial on Bayesian Optimization

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This paper cites Fr¨ ohlich, Melanie N.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Fr¨ ohlich, Melanie N

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This paper cites Fr¨ ohlich, Edgar D.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Fr¨ ohlich, Edgar D

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This paper cites Learning robust rewards with adversarial inverse reinforcement learning,.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Learning robust rewards with adversarial inverse reinforcement learning,

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This paper cites Bayesian Optimization with Inequality Constraints.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Bayesian Optimization with Inequality Constraints

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This paper cites Osborne, and Philipp Hennig.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Osborne, and Philipp Hennig

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Unresolved cited work

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Observation c0a25d99-8986-4e75-bd2a-84f1d70266b4 · outbound

This paper cites Identification for control: From the early achievements to the revival of experiment design.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Identification for control: From the early achievements to the revival of experiment design

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Observation 378c0845-6a81-4a2d-b0be-d0c77d64fd7a · outbound

This paper cites Multi-objective optimization of a path-following MPC for vehicle guidance: A Bayesian optimization approach.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Multi-objective optimization of a path-following MPC for vehicle guidance: A Bayesian optimization approach

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Observation a3714ab1-e776-4a58-9962-2c181298f6d7 · outbound

This paper cites Lawrence.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Lawrence

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Observation a2018f0b-1014-4e8e-8fb3-56adb8955b9c · outbound

This paper cites Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning

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Observation 18675a04-9d78-45a4-867d-9e776506cec3 · outbound

This paper cites A survey of actor-critic reinforcement learning: Standard and natural policy gradients.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents A survey of actor-critic reinforcement learning: Standard and natural policy gradients

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Observation a4aa8710-db08-4f3f-b9b7-30e220691a21 · outbound

This paper cites Learning for MPC with stability & safety guarantees.Automatica, 146:110598, 2022.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Learning for MPC with stability & safety guarantees.Automatica, 146:110598, 2022

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This paper cites Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality? IF AC-PapersOnLine, 53(2):8076– 8081, 2020.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality? IF AC-PapersOnLine, 53(2):8076– 8081, 2020

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Observation 59cf1326-66f8-46b6-a59b-94dda879d8e5 · outbound

This paper cites Data-Driven Economic NMPC Using Reinforcement Learning.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Data-Driven Economic NMPC Using Reinforcement Learning

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Observation 42ec66f3-8de9-44d8-9d9f-8b8044d6e82d · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Reinforcement learning for mixed-integer problems based on MPC

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Observation 4272abc0-ebae-4cb2-b051-432f2bef1fff · outbound

This paper cites Reinforcement learning based on MPC and the stochastic policy gradient method.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Reinforcement learning based on MPC and the stochastic policy gradient method

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Observation 1d02cf75-c4a2-4bbf-b276-f7fccaa57a8d · outbound

This paper cites Guerreiro, Carlos M.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Guerreiro, Carlos M

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This paper cites Evolutionary optimization of high- dimensional multiobjective and many-objective expensive problems assisted by a dropout neural network.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Evolutionary optimization of high- dimensional multiobjective and many-objective expensive problems assisted by a dropout neural network

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Observation 5d57e077-d5c2-48f0-b871-9801f5aabbec · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

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This paper cites Mastering diverse control tasks through world models.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Mastering diverse control tasks through world models

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents M¨ uller, and Petros Koumoutsakos

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Hayes et al

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Deep Gaussian process for multi-objective Bayesian optimization

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Wabersich, Marcel Menner, and Melanie N

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This paper cites Stability-informed Bayesian Optimization for MPC Cost Function Learning.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Stability-informed Bayesian Optimization for MPC Cost Function Learning

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Observation 5c7c6898-a4e8-4634-ad24-e03b9aee322f · outbound

This paper cites Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms

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This paper cites Multilayer feedforward networks are universal approximators.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Multilayer feedforward networks are universal approximators

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Observation 8b35d3e4-8d5b-418d-9257-caf1f423dee2 · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization

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Observation e5df7bd6-1aa3-4c4c-9b3f-a38304e3b418 · outbound

This paper cites Toward a theoretical foundation of policy optimization for learning control policies.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Toward a theoretical foundation of policy optimization for learning control policies

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL

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Observation c607f2fd-834d-4132-b10b-08553b2fbb0c · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Hoos, and Kevin Leyton- Brown

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents J., Santosh Penubothula, Chandramouli Kamanchi, and Shalabh Bhatnagar

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Observation 52639965-2c9f-4507-b5a9-096583ae1d7b · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents When to Trust Your Model: Model-Based Policy Optimization

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Bilevel optimization: Convergence analysis and enhanced design

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents BINOCULARS for efficient, nonmyopic sequential experimental design

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Unresolved cited work

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Pontryagin Differentiable Programming: An End- to-End Learning and Control Framework

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Observation 56f647ea-4e48-4fdd-b1df-7239a29065bf · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Data-efficient reinforcement learning with probabilistic model predictive control

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Observation 00b79c4c-7343-41b9-94ce-66e91a59bdc3 · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents A review on genetic algorithm: past, present, and future

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:28:55.476462Z

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Lekkas, and S´ ebastien Gros

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Observation 1c661c43-f00b-45f3-a849-90fe67464ae9 · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Doyle III

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:28:55.649869Z

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Observation 44ec2c76-757e-4fa2-9cd5-f570cd561f56 · outbound

This paper cites Huynh, Ali Mesbah, and Joel A.

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Huynh, Ali Mesbah, and Joel A

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:28:55.743538Z

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Observation 109af6cc-e0b4-4957-8b30-076b5ab235a9 · outbound

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Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents Lagoudakis and Ronald Parr

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