John D. Martin

Orcid: 0000-0002-9828-8203

According to our database1, John D. Martin authored at least 15 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning.
CoRR, 2024

2023
Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment.
CoRR, 2023

Settling the Reward Hypothesis.
Proceedings of the International Conference on Machine Learning, 2023

MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning.
Proceedings of the Conference on Robot Learning, 2023

2022
Should Models Be Accurate?
CoRR, 2022

2021
Modeling the onset of symptoms of COVID-19: Effects of SARS-CoV-2 variant.
PLoS Comput. Biol., 2021

Adapting the Function Approximation Architecture in Online Reinforcement Learning.
CoRR, 2021

2020
On Catastrophic Interference in Atari 2600 Games.
CoRR, 2020

Fusing Concurrent Orthogonal Wide-aperture Sonar Images for Dense Underwater 3D Reconstruction.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Variational Filtering with Copula Models for SLAM.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Stochastically Dominant Distributional Reinforcement Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

2018
Recursive Sparse Pseudo-input Gaussian Process SARSA.
CoRR, 2018

Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation.
Proceedings of the 2nd Annual Conference on Robot Learning, 2018

2017
Extending Model-based Policy Gradients for Robots in Heteroscedastic Environments.
Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017


  Loading...