James Queeney

James Queeney
Jimmy's Quick Links

  • Biography

    Jimmy conducts research on data-driven methods for decision making and control. During his PhD, he developed reliable deep reinforcement learning algorithms with guarantees on training stability, robustness, and safety.

  • Internships with Jimmy

    • ST0134: Internship - Generalization in Reinforcement Learning

      MERL is seeking a motivated and qualified individual to conduct research in the area of reinforcement learning (RL), with a focus on generalization. Topics include robustness, safety, and adaptation in single-agent or multi-agent applications. The ideal candidate will be a PhD student with a solid background in RL or imitation learning. Experience with deep RL implementations is a plus. Publication of the results produced during the internship is expected. Duration of the internship is expected to be 3 months. Start date is flexible.

    See All Internships at MERL

  • MERL Publications

    •  Ozcan, E.C., Giammarino, V., Queeney, J., Paschalidis, I.C., "A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations", IEEE Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-178 PDF
      • @inproceedings{Ozcan2024dec,
      • author = {Ozcan, Erhan Can and Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.}},
      • title = {A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-178}
      • }
    •  Queeney, J., Cai, X., Benosman, M., How, J.P., "GRAM: Generalization in Deep RL with a Robust Adaptation Module", arXiv, December 2024.
      BibTeX arXiv
      • @article{Queeney2024dec,
      • author = {Queeney, James and Cai, Xiaoyi and Benosman, Mouhacine and How, Jonathan P.}},
      • title = {GRAM: Generalization in Deep RL with a Robust Adaptation Module},
      • journal = {arXiv},
      • year = 2024,
      • month = dec,
      • url = {https://arxiv.org/abs/2412.04323}
      • }
    •  Queeney, J., Paschalidis, I.C., Cassandras, C.G., "Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse", arXiv, October 2024.
      BibTeX arXiv
      • @article{Queeney2024oct,
      • author = {Queeney, James and Paschalidis, Ioannis Ch. and Cassandras, Christos G.}},
      • title = {Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse},
      • journal = {arXiv},
      • year = 2024,
      • month = oct,
      • url = {https://arxiv.org/abs/2206.13714}
      • }
    •  Cai, X., Queeney, J., Xu, T., Datar, A., Pan, C., Miller, M., Flather, A., Osteen, P.R., Roy, N., Xiao, X., How, J.P., "PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain", arXiv, September 2024.
      BibTeX arXiv
      • @article{Cai2024sep,
      • author = {Cai, Xiaoyi and Queeney, James and Xu, Tong and Datar, Aniket and Pan, Chenhui and Miller, Max and Flather, Ashton and Osteen, Philip R. and Roy, Nicholas and Xiao, Xuesu and How, Jonathan P.}},
      • title = {PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain},
      • journal = {arXiv},
      • year = 2024,
      • month = sep,
      • url = {https://www.arxiv.org/abs/2409.03005}
      • }
    •  Giammarino, V., Queeney, J., Paschalidis, I.C., "Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning", arXiv, June 2024.
      BibTeX arXiv
      • @article{Giammarino2024jun2,
      • author = {Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.}},
      • title = {Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning},
      • journal = {arXiv},
      • year = 2024,
      • month = jun,
      • url = {https://arxiv.org/abs/2407.12792}
      • }
    See All MERL Publications for Jimmy

  • Other Publications

    •  Vittorio Giammarino, James Queeney, Lucas C. Carstensen, Michael E. Hasselmo and Ioannis Ch. Paschalidis, "Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation", 22nd IFAC World Congress, 2023.
      BibTeX
      • @Inproceedings{giammarino_2023_ifac,
      • author = {Giammarino, Vittorio and Queeney, James and Carstensen, Lucas C. and Hasselmo, Michael E. and Paschalidis, Ioannis Ch.},
      • title = {Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation},
      • booktitle = {22nd IFAC World Congress},
      • year = 2023
      • }
    •  Vittorio Giammarino, James Queeney and Ioannis Ch. Paschalidis, "Adversarial Imitation Learning from Visual Observations using Latent Information", 2023.
      BibTeX
      • @Misc{giammarino_2023_laifo,
      • author = {Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.},
      • title = {Adversarial Imitation Learning from Visual Observations using Latent Information},
      • year = 2023
      • }
    •  James Queeney, "Reliable Deep Reinforcement Learning: Stable Training and Robust Deployment", 2023, Boston University.
      BibTeX
      • @Phdthesis{queeney_2023_dissertation,
      • author = {Queeney, James},
      • title = {Reliable Deep Reinforcement Learning: Stable Training and Robust Deployment},
      • school = {Boston University},
      • year = 2023
      • }
    •  James Queeney, Erhan Can Ozcan, Ioannis Ch. Paschalidis and Christos G. Cassandras, "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", 2023.
      BibTeX
      • @Misc{queeney_2023_otp,
      • author = {Queeney, James and Ozcan, Erhan Can and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
      • title = {Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees},
      • year = 2023
      • }
    •  James Queeney and Mouhacine Benosman, "Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning", Advances in Neural Information Processing Systems, 2023, vol. 36.
      BibTeX
      • @Inproceedings{queeney_2023_ramu,
      • author = {Queeney, James and Benosman, Mouhacine},
      • title = {Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning},
      • booktitle = {Advances in Neural Information Processing Systems},
      • year = 2023,
      • volume = 36,
      • publisher = {Curran Associates, Inc.}
      • }
    •  James Queeney, Ioannis Ch. Paschalidis and Christos G. Cassandras, "Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse", 2022.
      BibTeX
      • @Misc{queeney_2022_gpi,
      • author = {Queeney, James and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
      • title = {Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse},
      • year = 2022
      • }
    •  James Queeney, Ioannis Ch. Paschalidis and Christos G. Cassandras, "Generalized Proximal Policy Optimization with Sample Reuse", Advances in Neural Information Processing Systems, 2021, vol. 34.
      BibTeX
      • @Inproceedings{queeney_2021_geppo,
      • author = {Queeney, James and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
      • title = {Generalized Proximal Policy Optimization with Sample Reuse},
      • booktitle = {Advances in Neural Information Processing Systems},
      • year = 2021,
      • volume = 34,
      • publisher = {Curran Associates, Inc.}
      • }
    •  James Queeney, Ioannis Ch. Paschalidis and Christos G. Cassandras, "Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach", Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, pp. 9377-9385.
      BibTeX
      • @Inproceedings{queeney_2021_uatrpo,
      • author = {Queeney, James and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
      • title = {Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach},
      • booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
      • year = 2021,
      • volume = 35,
      • pages = {9377--9385},
      • publisher = {{AAAI} Press}
      • }

  • Software & Data Downloads