Publications

paper slides poster video Wijesundara, Baisero, Castañón, Carlin, Platt, and Amato, “Leveraging Fully Observable Solutions for Improved Partially Observable Offline Reinforcement Learning,” in (Under Review) Transactions on Machine Learning Research, 2025.
paper slides poster video Baisero, Bhati, Liu, Pillai, and Amato, “Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning,” in (Preprint), 2025.
paper slides poster video Baisero, “Role of State in Partially Observable Reinforcement Learning,” in (Extended Abstract, Doctoral Consortium) Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2025.
paper slides poster video Marchesini, Baisero, Bhati, and Amato, “On Stateful Value Factorization in Multi-Agent Reinforcement Learning,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2025.
paper slides poster video Wijesundara, Baisero, Castañón, Carlin, Platt, and Amato, “Leveraging Fully Observable Solutions for Improved Partially Observable Offline Reinforcement Learning,” in (Extended Abstract) Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2025.
paper slides poster video Nguyen, Baisero, Klee, Wang, Platt, and Amato, “Equivariant Reinforcement Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2023.
paper slides poster video Lyu, Baisero, Xiao, Daley, and Amato, “On Centralized Critics in Multi-Agent Reinforcement Learning,” Journal of Artificial Intelligence Research, 2023.
paper slides poster video Nguyen, Baisero, Wang, Amato, and Platt, “Leveraging Fully Observable Policies for Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2022.
paper slides poster video Baisero, Daley, and Amato, “Asymmetric DQN for Partially Observable Reinforcement Learning,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2022.
paper slides poster video 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.
paper slides poster video 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.
paper slides poster video Baisero and Amato, “Unbiased Asymmetric Reinforcement Learning under Partial Observability,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2022.
paper slides poster video Baisero and Amato, “Reconciling Rewards with Predictive State Representations,” in Proceedings of the International Joint Conference on Artificial Intelligence, 2021.
paper slides poster video Baisero and Amato, “Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning,” in (Extended Abstract) Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2020.
paper slides poster video Amato and Baisero, “Active Goal Recognition,” arXiv preprint arXiv:1909.11173, 2019.
paper slides poster video Baisero and Amato, “Learning Internal State Models in Partially Observable Environments,” in Reinforcement Learning under Partial Observability, NeurIPS Workshop, 2018.
paper slides poster video Baisero, Otte, Englert, and Toussaint, “Identification of Unmodeled Objects from Symbolic Descriptions,” arXiv preprint arXiv:1701.06450, 2017.
paper slides poster 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.
paper slides poster video 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.
paper slides poster video Baisero, Pokorny, and Ek, “On a Family of Decomposable Kernels on Sequences,” arXiv preprint arXiv:1501.06284, 2015.
paper slides poster video Baisero, Pokorny, Kragic, and Ek, “The Path Kernel: A Novel Kernel for Sequential Data,” in Pattern Recognition Application and Methods, 2015.
paper slides poster video Baisero, Pokorny, Kragic, and Ek, “The Path Kernel,” in International Conference on Pattern Recognition Applications and Methods, 2013.
paper slides poster video Baisero, “Encoding Sequential Structures using Kernels,” KTH Royal Institute of Technology, 2012.