Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces | Paper Citation Record

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This paper cites The neural tangent kernel in high dimensions: Triple descent and a multi-scale theory of generalization.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces The neural tangent kernel in high dimensions: Triple descent and a multi-scale theory of generalization

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This paper cites Agrachev and Yu.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Agrachev and Yu

Reference 2

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This paper cites Akametalu, Shahab Kaynama, Jaime Fern \'a ndez Fisac, Melanie Nicole Zeilinger, Jeremy H.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Akametalu, Shahab Kaynama, Jaime Fern \'a ndez Fisac, Melanie Nicole Zeilinger, Jeremy H

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This paper cites Learning and generalization in overparameterized neural networks, going beyond two layers.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Learning and generalization in overparameterized neural networks, going beyond two layers

Reference 4

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This paper cites A convergence theory for deep learning via over-parameterization.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces A convergence theory for deep learning via over-parameterization

Reference 5

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This paper cites Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control

Reference 6

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Robust locally-linear controllable embedding

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This paper cites Efficient Representation of Low-Dimensional Manifolds using Deep Networks.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Efficient Representation of Low-Dimensional Manifolds using Deep Networks

Reference 8

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This paper cites High-dimensional limit theorems for sgd: Effective dynamics and critical scaling.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces High-dimensional limit theorems for sgd: Effective dynamics and critical scaling

Reference 9

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This paper cites Dynamic programming and optimal control: Volume I, volume 4.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Dynamic programming and optimal control: Volume I, volume 4

Reference 10

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This paper cites Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming

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This paper cites An introduction to aspects of geometric control theory.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces An introduction to aspects of geometric control theory

Reference 12

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces An introduction to differentiable manifolds and Riemannian geometry

Reference 13

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This paper cites Wilkinson.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Wilkinson

Reference 14

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This paper cites Brockett.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Brockett

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This paper cites OpenAI Gym.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces OpenAI Gym

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This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

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This paper cites Deep Networks and the Multiple Manifold Problem.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Deep Networks and the Multiple Manifold Problem

Reference 18

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This paper cites Geometric control of mechanical systems: modeling, analysis, and design for simple mechanical control systems, volume 49.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Geometric control of mechanical systems: modeling, analysis, and design for simple mechanical control systems, volume 49

Reference 19

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This paper cites Manifold embeddings for model-based reinforcement learning under partial observability.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Manifold embeddings for model-based reinforcement learning under partial observability

Reference 20

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This paper cites Cai, Zhuoran Yang, Jason Lee, and Zhaoran Wang.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Cai, Zhuoran Yang, Jason Lee, and Zhaoran Wang

Reference 21

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This paper cites Lee, and Zhaoran Wang.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Lee, and Zhaoran Wang

Reference 22

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This paper cites Carlsson, T.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Carlsson, T

Reference 23

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This paper cites Using bisimulation for policy transfer in mdps.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Using bisimulation for policy transfer in mdps

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This paper cites Redunet: A white-box deep network from the principle of maximizing rate reduction.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Redunet: A white-box deep network from the principle of maximizing rate reduction

Reference 25

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This paper cites Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery

Reference 26

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This paper cites Analysis and design of nonlinear control systems.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Analysis and design of nonlinear control systems

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This paper cites Stochastic gradient and langevin processes.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Stochastic gradient and langevin processes

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This paper cites On the global convergence of gradient descent for over-parameterized models using optimal transport.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces On the global convergence of gradient descent for over-parameterized models using optimal transport

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This paper cites A deep network construction that adapts to intrinsic dimensionality beyond the domain.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces A deep network construction that adapts to intrinsic dimensionality beyond the domain

Reference 30

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

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This paper cites Pilco: A model-based and data-efficient approach to policy search.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Pilco: A model-based and data-efficient approach to policy search

Reference 32

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Reinforcement learning in continuous time and space

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Reinforcement learning in continuous time and space

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Gradient Descent Finds Global Minima of Deep Neural Networks

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

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This paper cites Estimating the intrinsic dimension of datasets by a minimal neighborhood information.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Estimating the intrinsic dimension of datasets by a minimal neighborhood information

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Fefferman, S

Reference 38

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Panangaden, and Doina Precup

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This paper cites Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces DeepMDP: Learning Continuous Latent Space Models for Representation Learning

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Dean, and Matthew Greig

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This paper cites Modelling the influence of data structure on learning in neural networks: the hidden manifold model.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Modelling the influence of data structure on learning in neural networks: the hidden manifold model

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces InfoBot: Transfer and Exploration via the Information Bottleneck

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget

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This paper cites Differential Topology.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Differential Topology

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Abbeel, and Sergey Levine

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Abbeel, and Sergey Levine

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Finite Depth and Width Corrections to the Neural Tangent Kernel

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Gaussian error linear units (gelus)

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Safe reinforcement learning on autonomous vehicles

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Nonlinear control systems: an introduction

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Manfred Otto Heess, and Alex Lamb

Reference 56

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Gabriel, and C

Reference 57

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

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This paper cites Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Policy gradient and actor-critic learning in continuous time and space: Theory and algorithms

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Policy gradient and actor-critic learning in continuous time and space: Theory and algorithms

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces q-learning in continuous time

Reference 62

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Observation 948edcbd-5ed7-4ca4-9fe1-2faa77a81189 · outbound

This paper cites Machado, and George Dimitri Konidaris.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Machado, and George Dimitri Konidaris

Reference 63

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Observation 2b86181b-5ce2-4bb3-a52a-e989f1d78995 · outbound

This paper cites Geometric control theory.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Geometric control theory

Reference 64

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Observation 721b182a-a593-4aa5-a06c-e82dc64cdaa8 · outbound

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

Reference 65

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Observation 85cefb57-eb36-4e86-9214-590ae3f2c417 · outbound

This paper cites On the general theory of control systems.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces On the general theory of control systems

Reference 66

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Observation 63ebd142-7283-431b-85be-318f0072a0bc · outbound

This paper cites Brownian motion and stochastic calculus, volume 113.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Brownian motion and stochastic calculus, volume 113

Reference 67

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Observation 1bba5805-7e3d-4091-a553-a7f6f1105538 · outbound

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

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Champion-level drone racing using deep reinforcement learning

Reference 68

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Observation 2753443b-2e3a-4201-8966-377c87870e2f · outbound

This paper cites Actor-critic algorithms.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Actor-critic algorithms

Reference 69

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Observation 9fca9d99-69f2-4503-a96c-db6e6512667d · outbound

This paper cites Bellemare, and Pablo Samuel Castro.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Bellemare, and Pablo Samuel Castro

Reference 70

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Observation 89a122e8-b1cc-451d-ab03-194395c7c7ea · outbound

This paper cites Deep Neural Networks as Gaussian Processes.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Deep Neural Networks as Gaussian Processes

Reference 71

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Observation d4448537-51a4-4e6f-bdac-0a848020d41f · outbound

This paper cites Wide neural networks of any depth evolve as linear models under gradient descent.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Wide neural networks of any depth evolve as linear models under gradient descent

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.186382Z

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Observation 5fc29351-5b4b-4792-8835-3e7aa74a33e2 · outbound

This paper cites End-to-End Training of Deep Visuomotor Policies.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces End-to-End Training of Deep Visuomotor Policies

Reference 73

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Observation 249eee52-e398-48db-83d9-1230ed2cc91c · outbound

This paper cites Convergence analysis of two-layer neural networks with relu activation.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Convergence analysis of two-layer neural networks with relu activation

Reference 74

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Observation 481ffa8c-ac0a-40d6-96f3-a683d85ebb39 · outbound

This paper cites Continuous control with deep reinforcement learning.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Continuous control with deep reinforcement learning

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.202353Z

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Observation 3d902293-65b4-4ce7-ac8c-0f3e35923b53 · outbound

This paper cites Robot reinforcement learning on the constraint manifold.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Robot reinforcement learning on the constraint manifold

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.206240Z

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Observation 551c0001-0c3c-496c-bb21-2b6f5c3c748c · outbound

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

Reference 78

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Observation c3bd6b6a-7bdd-45a3-a061-13c9c0647c5a · outbound

This paper cites Segmentation of multivariate mixed data via lossy data coding and compression.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Segmentation of multivariate mixed data via lossy data coding and compression

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.213977Z

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Observation 1d8863d3-150e-436e-9137-53a73fb1006a · outbound

This paper cites A Laplacian Framework for Option Discovery in Reinforcement Learning.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces A Laplacian Framework for Option Discovery in Reinforcement Learning

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.218530Z

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Observation d8400d7f-3e3b-4d0b-8397-b22e6845d0e0 · outbound

This paper cites Eigenoption Discovery through the Deep Successor Representation.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Eigenoption Discovery through the Deep Successor Representation

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.222723Z

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Observation 68381462-0967-4edf-9eb5-04461aa72ab5 · outbound

This paper cites Proto-value functions: developmental reinforcement learning.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Proto-value functions: developmental reinforcement learning

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.226702Z

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Observation db08da4b-e919-4e4d-9b5d-c4dc93897eb1 · outbound

This paper cites Proto-value functions: A laplacian framework for learning representation and control in markov decision processes.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Proto-value functions: A laplacian framework for learning representation and control in markov decision processes

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.230316Z

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Observation 00498fb3-d68d-46d7-ae4e-eda7e526abdd · outbound

This paper cites Approximate gradient methods in policy-space optimization of markov reward processes.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Approximate gradient methods in policy-space optimization of markov reward processes

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.233902Z

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Observation 7889a8d7-e95c-48c9-b11d-6b55c10d6425 · outbound

This paper cites A mean field view of the landscape of two-layer neural networks.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces A mean field view of the landscape of two-layer neural networks

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:06.534016Z

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Observation 0a70f1fa-3a35-4fbb-b718-ee1c48e424a2 · outbound

This paper cites A mean field view of the landscape of two-layer neural networks.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces A mean field view of the landscape of two-layer neural networks

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:06.445200Z

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Observation 5bb4512c-e3c0-44c0-80eb-b7fe66e2d5c2 · outbound

This paper cites Rusu, Joel Veness, Marc G.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Rusu, Joel Veness, Marc G

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:06.266209Z

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Observation 617e6dbb-4e78-4a87-9904-4effbcd11578 · outbound

This paper cites A case study in approximate linearization: The acrobat example.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces A case study in approximate linearization: The acrobat example

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:06.063336Z

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Observation 72414ee1-42a6-49cb-bb42-00519f9dcd5e · outbound

This paper cites Nair and Geoffrey E.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Nair and Geoffrey E

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:05.887242Z

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Observation a6933872-83c9-44b5-86d9-2c647672107b · outbound

This paper cites Non-linear dynamical control systems.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Non-linear dynamical control systems

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:05.709793Z

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Observation ee69a4f9-df4d-4e1a-a7ac-571686b8386c · outbound

This paper cites Geometric compression of invariant manifolds in neural networks.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Geometric compression of invariant manifolds in neural networks

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:05.542471Z

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Observation 9019261e-98a3-4c24-9c76-ca8987ea43da · outbound

This paper cites Masked completion via structured diffusion with white-box transformers.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Masked completion via structured diffusion with white-box transformers

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:05.322095Z

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Observation 4a23057f-6955-4503-9557-feefa8953357 · outbound

This paper cites Bronstein, and Ron Kimmel.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Bronstein, and Ron Kimmel

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:05.177709Z

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Observation fe315fec-f990-4265-a9ff-ff3e916b4b98 · outbound

This paper cites A contraction theory approach to stochastic incremental stability.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces A contraction theory approach to stochastic incremental stability

Reference 94

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T13:21:59.805638Z

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

Reference 95

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:22:05.031893Z

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Observation 2a571ed7-a38e-42de-b536-0e5d5a68b13f · outbound

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

Reference 96

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Observation 1b2b0728-c285-496d-ba1a-44a16968fe52 · outbound

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Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:22:04.796428Z

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Observation c3516e98-bfa9-44e0-b99f-e3f188c2edb1 · outbound

This paper cites Controllability of dynamical systems with constraints.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Controllability of dynamical systems with constraints

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:04.662542Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d7c6e26-645a-4788-837b-44e5e543e107 · outbound

This paper cites Robbin, Uw Madison, and Dietmar A.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Robbin, Uw Madison, and Dietmar A

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:04.513009Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fca51baf-0015-4f99-9521-56001d4c9882 · outbound

This paper cites Deep ReLU network approximation of functions on a manifold.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Deep ReLU network approximation of functions on a manifold

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.297979Z

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Observation 9ea839da-ea4b-45eb-b3c9-9d34e1dd3306 · outbound

This paper cites Trust Region Policy Optimization.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Trust Region Policy Optimization

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.302552Z

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