Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping | Paper Citation Record

Observation 1bbc07d9-ebb0-4236-b113-96f8d7a09c60 · outbound

This paper cites Outplaying elite table tennis players with an autonomous robot,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Outplaying elite table tennis players with an autonomous robot,

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This paper cites Champion- level drone racing using deep reinforcement learning,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Champion- level drone racing using deep reinforcement learning,

Reference 2

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Precise and dexterous robotic manipulation via human-in-the- loop reinforcement learning,

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Outracing champion gran turismo drivers with deep reinforcement learning,

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This paper cites Anymal parkour: Learning agile navigation for quadrupedal robots,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Anymal parkour: Learning agile navigation for quadrupedal robots,

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Learning agile and dynamic motor skills for legged robots,

Reference 6

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This paper cites Safe reinforcement learning for arm manipulation with constrained markov decision process,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Safe reinforcement learning for arm manipulation with constrained markov decision process,

Reference 7

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Safety-polarized and prioritized reinforcement learning,

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This paper cites Safe reinforcement learning via projection on a safe set: How to achieve optimality?.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Safe reinforcement learning via projection on a safe set: How to achieve optimality?

Reference 9

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This paper cites Data-driven mpc for quadrotors,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Data-driven mpc for quadrotors,

Reference 10

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This paper cites Learning-based model predictive control: Toward safe learning in control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Learning-based model predictive control: Toward safe learning in control,

Reference 11

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This paper cites Model predictive control: Theory and practice—a survey,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Model predictive control: Theory and practice—a survey,

Reference 12

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This paper cites Constrained model predictive control: Stability and optimality,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Constrained model predictive control: Stability and optimality,

Reference 13

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This paper cites Borrelli, A.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Borrelli, A

Reference 14

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This paper cites Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification

Reference 15

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Observation 6d34f5f2-9027-460d-b25b-04380b0c897e · outbound

This paper cites Value function approximation and model predictive control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Value function approximation and model predictive control,

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This paper cites Ac4mpc: Actor-critic reinforcement learning for guiding model predictive control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Ac4mpc: Actor-critic reinforcement learning for guiding model predictive control,

Reference 17

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This paper cites Plan online, learn offline: Efficient learning and exploration via model-based control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Plan online, learn offline: Efficient learning and exploration via model-based control,

Reference 18

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This paper cites An overview of the action space for deep reinforcement learning,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping An overview of the action space for deep reinforcement learning,

Reference 19

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This paper cites Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments,

Reference 20

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This paper cites Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios,

Reference 21

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Observation e2346ac2-a7e7-429c-8561-b2740bf6a9e1 · outbound

This paper cites A hierarchical approach for strategic motion planning in autonomous racing,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping A hierarchical approach for strategic motion planning in autonomous racing,

Reference 22

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This paper cites Parameterization approach of the frenet transformation for model predictive control of autonomous vehicles,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Parameterization approach of the frenet transformation for model predictive control of autonomous vehicles,

Reference 23

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This paper cites Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control,

Reference 24

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This paper cites Rein- forcement learning-based receding horizon control using adaptive control barrier functions for safety-critical systems,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Rein- forcement learning-based receding horizon control using adaptive control barrier functions for safety-critical systems,

Reference 25

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This paper cites Adaptive stochastic nonlinear model predictive control with look-ahead deep reinforcement learning for autonomous vehicle motion control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Adaptive stochastic nonlinear model predictive control with look-ahead deep reinforcement learning for autonomous vehicle motion control,

Reference 26

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This paper cites Learning when to drive in intersections by combining reinforcement learning and model predictive control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Learning when to drive in intersections by combining reinforcement learning and model predictive control,

Reference 27

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This paper cites Safe reinforcement learning with chance- constrained model predictive control,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Safe reinforcement learning with chance- constrained model predictive control,

Reference 28

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping A safe reinforcement learning driven weights-varying model predictive control for autonomous vehicle motion control,

Reference 29

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This paper cites Learning robot trajectories subject to kinematic joint constraints,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Learning robot trajectories subject to kinematic joint constraints,

Reference 30

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Action mapping: A reinforcement learning method for constrained-input systems,

Reference 31

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This paper cites Action mapping for reinforcement learning in continuous environments with constraints,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Action mapping for reinforcement learning in continuous environments with constraints,

Reference 32

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Optimal transportation by orthogonal coupling dynamics,

Reference 33

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This paper cites Learning for control: An inverse optimization approach,.

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Learning for control: An inverse optimization approach,

Reference 34

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Inverse optimization via learning feasible regions,

Reference 35

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Unresolved cited work

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Unresolved cited work

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Spivak,Calculus, 4th ed

Reference 38

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Unresolved cited work

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Method and apparatus for low-complexity trajectory planning,

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping A dual active-set solver for embedded quadratic programming using recursive LDLT updates,

Reference 41

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping The quickhull algorithm for convex hulls,

Reference 42

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping qpsolvers: Quadratic Programming Solvers in Python,

Reference 43

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Universal value function approximators,

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 45

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Bigger, regularized, optimistic: scaling for compute and sample efficient continuous control,

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 47

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Intrinsic benefits of categorical distributional loss: Uncertainty-aware regularized exploration in reinforcement learning,

Reference 48

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping Stop regressing: Training value functions via classification for scalable deep rl,

Reference 49

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Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping 1000 layer networks for self-supervised rl: Scaling depth can enable new goal-reaching capabilities,

Reference 50

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