Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models | Paper Citation Record

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This paper cites Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments

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This paper cites Autonomous Drone Racing: A Survey.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Autonomous Drone Racing: A Survey

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This paper cites A Systematic Study on Reinforcement Learning Based Applications.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models A Systematic Study on Reinforcement Learning Based Applications

Reference 3

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Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Unresolved cited work

Reference 4

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This paper cites Artificial Intelligence for Trusted Autonomous Satellite Operations.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Artificial Intelligence for Trusted Autonomous Satellite Operations

Reference 5

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This paper cites Reinforced Model Predictive Guidance and Control for Spacecraft Proximity Operations.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Reinforced Model Predictive Guidance and Control for Spacecraft Proximity Operations

Reference 6

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This paper cites Reinforcement learning in spacecraft control applica- tions: Advances, prospects, and challenges.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Reinforcement learning in spacecraft control applica- tions: Advances, prospects, and challenges

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This paper cites An on-orbit servicing framework for satellite collision avoidance: To- wards autonomous mission planning with reinforcement learning.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models An on-orbit servicing framework for satellite collision avoidance: To- wards autonomous mission planning with reinforcement learning

Reference 8

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This paper cites Adaptive pinpoint and fuel efficient mars landing using reinforce- ment learning.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Adaptive pinpoint and fuel efficient mars landing using reinforce- ment learning

Reference 9

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This paper cites Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UA Vs: Field Experiments.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UA Vs: Field Experiments

Reference 10

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This paper cites Sim-to-Real Reinforcement Learning for Deformable Object Manipulation.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

Reference 11

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This paper cites Quantifying the Reality Gap in Robotic Manipulation Tasks.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Quantifying the Reality Gap in Robotic Manipulation Tasks

Reference 12

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This paper cites In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI).

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI)

Reference 13

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This paper cites Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Tech- niques for Reinforcement Learning in Humanoid Bipedal Locomotion.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Tech- niques for Reinforcement Learning in Humanoid Bipedal Locomotion

Reference 14

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This paper cites Towards Sample-Efficiency and General- ization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Towards Sample-Efficiency and General- ization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review

Reference 15

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This paper cites A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 16

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This paper cites Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity

Reference 17

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This paper cites an unresolved cited work.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Unresolved cited work

Reference 18

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This paper cites Jategaonkar,Flight Vehicle System Identification: A Time Domain Methodology, pp.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Jategaonkar,Flight Vehicle System Identification: A Time Domain Methodology, pp

Reference 19

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This paper cites Generative Adversarial Networks.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Generative Adversarial Networks

Reference 20

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This paper cites Generative Adversarial Networks: An Overview.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Generative Adversarial Networks: An Overview

Reference 21

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This paper cites An Introduction to Variational Autoencoders.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models An Introduction to Variational Autoencoders

Reference 22

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This paper cites Face generation and editing with stylegan: A survey.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Face generation and editing with stylegan: A survey

Reference 23

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This paper cites NVAE: A Deep Hierarchical Variational Autoencoder.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models NVAE: A Deep Hierarchical Variational Autoencoder

Reference 24

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This paper cites Sig-Wasserstein GANs for Time Series Generation.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Sig-Wasserstein GANs for Time Series Generation

Reference 25

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This paper cites Training Generative Adversarial Networks with Limited Data.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Training Generative Adversarial Networks with Limited Data

Reference 26

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This paper cites Cuomo, V.S.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Cuomo, V.S

Reference 27

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This paper cites Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach

Reference 28

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This paper cites Characterizing possible failure modes in physics-informed neural networks.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Characterizing possible failure modes in physics-informed neural networks

Reference 29

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This paper cites Case Studies of Generative Machine Learning Models for Dynamical Systems.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Case Studies of Generative Machine Learning Models for Dynamical Systems

Reference 30

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This paper cites Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models

Reference 31

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This paper cites Reinforcement Learning.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Reinforcement Learning

Reference 32

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Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Unresolved cited work

Reference 33

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This paper cites Inverse Reinforcement Learning for Minimum-Exposure Paths in Spatiotemporally Varying Scalar Fields.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Inverse Reinforcement Learning for Minimum-Exposure Paths in Spatiotemporally Varying Scalar Fields

Reference 34

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This paper cites Behavior Proximal Policy Optimization.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Behavior Proximal Policy Optimization

Reference 35

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This paper cites Policy Gradient Methods for Reinforcement Learning with Function Approximation.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Policy Gradient Methods for Reinforcement Learning with Function Approximation

Reference 36

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This paper cites Proximal Policy Optimization Algorithms.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Proximal Policy Optimization Algorithms

Reference 37

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This paper cites On-Policy vs. Off-Policy Deep Reinforcement Learning for Resource Allocation in Open Radio Access Network.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models On-Policy vs. Off-Policy Deep Reinforcement Learning for Resource Allocation in Open Radio Access Network

Reference 38

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This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 39

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This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 40

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This paper cites Improved SARSA and DQN algorithms for reinforcement learning.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Improved SARSA and DQN algorithms for reinforcement learning

Reference 41

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This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 42

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This paper cites Stable-Baselines3: Re- liable Reinforcement Learning Implementations.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Stable-Baselines3: Re- liable Reinforcement Learning Implementations

Reference 43

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This paper cites Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping

Reference 44

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This paper cites An Empirical Study on Generalizations of the ReLU Ac- tivation Function.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models An Empirical Study on Generalizations of the ReLU Ac- tivation Function

Reference 45

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