Publications
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Marchesini, Baisero, Bhati, and Amato, “On Stateful Value Factorization in Multi-Agent Reinforcement Learning,” in Proceedings of the Conference on Neural Information Processing Systems (under review), 2024. |
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Wijesundara, Baisero, Castañón, Carlin, Platt, and Amato, “Leveraging Fully Observable Solutions for Improved Partially Observable Offline Reinforcement Learning,” in International Conference on Machine Learning (under review), 2024. |
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Nguyen, Baisero, Klee, Wang, Platt, and Amato, “Equivariant Reinforcement Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2023. |
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Lyu, Baisero, Xiao, Daley, and Amato, “On Centralized Critics in Multi-Agent Reinforcement Learning,” Journal of Artificial Intelligence Research, 2023. |
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Nguyen, Baisero, Wang, Amato, and Platt, “Leveraging Fully Observable Policies for Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2022. |
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Baisero, Daley, and Amato, “Asymmetric DQN for Partially Observable Reinforcement Learning,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2022. |
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Nguyen, Yang, Baisero, Ma, Platt, and Amato, “Hierarchical Reinforcement Learning under Mixed Observability,” in Proceedings of the International Workshop on the Algorithmic Foundations of Robotics, 2022. |
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Lyu, Baisero, Xiao, and Amato, “A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2022. |
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video | Baisero and Amato, “Unbiased Asymmetric Reinforcement Learning under Partial Observability,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2022. |
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Baisero and Amato, “Reconciling Rewards with Predictive State Representations,” in Proceedings of the International Joint Conference on Artificial Intelligence, 2021. |
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video | Baisero and Amato, “Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2020. |
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Amato and Baisero, “Active Goal Recognition,” arXiv preprint arXiv:1909.11173, 2019. |
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Baisero and Amato, “Learning Internal State Models in Partially Observable Environments,” in Reinforcement Learning under Partial Observability, NeurIPS Workshop, 2018. |
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video | Baisero, Otte, Englert, and Toussaint, “Identification of Unmodeled Objects from Symbolic Descriptions,” arXiv preprint arXiv:1701.06450, 2017. |
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video | Baisero, Mollard, Lopes, Toussaint, and Lütkebohle, “Temporal Segmentation of Pair-Wise Interaction Phases in Sequential Manipulation Demonstrations,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015. |
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Mollard, Munzer, Baisero, Toussaint, and Lopes, “Robot Programming from Demonstration, Feedback and Transfer,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015. |
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Baisero, Pokorny, and Ek, “On a Family of Decomposable Kernels on Sequences,” arXiv preprint arXiv:1501.06284, 2015. |
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Baisero, Pokorny, Kragic, and Ek, “The Path Kernel: A Novel Kernel for Sequential Data,” in Pattern Recognition Application and Methods, 2015. |