Publications & Posters
* denotes joint first authorship.
2023
Settling the Reward Hypothesis,
Michael Bowling*, John D. Martin*, David Abel, Will Dabney,
ICML (2023) (Oral) [pdf]
MOTO: Offline to Online Fine-tuning for Model-Based Reinforcement Learning,
Rafael Rafailov, Kyle Beltran Hatch, Victor Kolev, John D Martin, Mariano Phielipp, Chelsea Finn,
ICLR Reincarnating Reinforcement Learning Workshop (2023) [pdf]
Model-based Adversarial Imitation Learning as Online Fine-tuning,
Rafael Rafailov, Victor Kolev, Kyle Beltran Hatch, John D Martin, Mariano Phielipp, Jiajun Wu, Chelsea Finn,
ICLR Reincarnating Reinforcement Learning Workshop (2023) [pdf]
2022
Settling the Reward Hypothesis,
Michael Bowling*, John D. Martin*, David Abel, Will Dabney,
ArXiv (2022) [pdf]
Learning to Prioritize Planning Updates in Model-based Reinforcement Learning,
Brad Burega, John D. Martin, Michael Bowling,
NeurIPS Workshop on Meta Learning (2022) [pdf]
Time to Take Embodiment Seriously,
John D. Martin,
The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (2022) [pdf]
Should Models Be Accurate?,
Esra’a Saleh, John D. Martin, Anna Koop, Arash Pourzarabi, Michael Bowling,
The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (2022) [pdf]
The Stochastic Road Network Environment for Robust Reinforcement Learning,
John D. Martin, Paul Szenher, Xi Lin, Brendan Englot,
ICRA Workshop on Releasing Robots into the Wild (2022) [pdf]
2021
Adapting the Function Approximation Architecture in Online Reinforcement Learning,
John D. Martin*, Joseph Modayil*,
ArXiv (2021) [pdf] [code] [RLDM 2022 pdf]
Reinforcement Learning Algorithms for Representing and Managing Uncertainty in Robotics,
John D. Martin,
Ph.D. Thesis, Stevens Institute of Technology, (2021) [pdf]
2020
Stochastically Dominant Distributional Reinforcement Learning,
John D. Martin, Michal Lyskawinski, Xiaohu Li, Brendan Englot,
37th International Conference on Machine Learning (2020) [pdf] [poster]
Variational Filtering with Copula Models for SLAM,
John D. Martin*, Kevin Doherty*, Caralyn Cyr, Brendan Englot, John Leonard
International Conference on Intelligent Robots and Systems (IROS) (2020) [pdf]
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs,
Fanfei Chen, John D. Martin, Yewei Huang, Jinkun Wang, Brendan Englot,
International Conference on Intelligent Robots and Systems (IROS) (2020) [pdf]
Fusing Concurrent Orthogonal Wide-aperture Sonar Images for Dense Underwater 3D Reconstruction,
John McConnell, John D. Martin, Brendan Englot,
International Conference on Intelligent Robots and Systems (IROS) (2020) [pdf]
On Catastrophic Interference in Atari 2600 Games,
William Fedus and Dibya Ghosh, John D. Martin, Marc G. Bellemare, Yoshua Bengio, Hugo Larochelle,
ArXiv (2020) [pdf], [code], [slides]
2018
Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation,
John D. Martin, Jinkun Wang, Brendan Englot,
Conference on Robot Learning (2018) [pdf]
Distributed Gaussian Process Temporal Differences for Actor-critic Learning,
John D. Martin, Zheng Xing, Zhiyuan Yao, Ionut Florescu, Brendan Englot,
New York Academy of Sciences Machine Learning Symposium (2018) [poster]
2017
Extending Model-based Policy Gradients for Robots in Heteroscedastic Environments,
John D. Martin, Brendan Englot,
Conference on Robot Learning (2017) [pdf]
Predicting Ocean Currents for Robot Navigation,
John D. Martin, Tixiao Shan, Brendan Englot,
Stevens Graduate Research Conference (2017) [poster]