Baisero, Bhati, Liu, Pillai, and Amato, “Fixing Value Function Decomposition for Multi-Agent Reinforcement Learning,” in (Under Review) International Conference on Machine Learning, 2025.
Marchesini, Baisero, Bhati, and Amato, “On Stateful Value Factorization in Multi-Agent Reinforcement Learning,” in (To Appear) Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2025.
Wijesundara, Baisero, Castañón, Carlin, Platt, and Amato, “Leveraging Fully Observable Solutions for Improved Partially Observable Offline Reinforcement Learning,” in (To Appear) (Extended Abstract) Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2025.
Nguyen, Baisero, Klee, Wang, Platt, and Amato, “Equivariant Reinforcement Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2023.
Nguyen, Baisero, Wang, Amato, and Platt, “Leveraging Fully Observable Policies for Learning under Partial Observability,” in Proceedings of the Conference on Robot Learning, 2022.
Baisero, Daley, and Amato, “Asymmetric DQN for Partially Observable Reinforcement Learning,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2022.
Nguyen, Yang, Baisero, Ma, Platt, and Amato, “Hierarchical Reinforcement Learning under Mixed Observability,” in (Workshop) Proceedings of the International Workshop on the Algorithmic Foundations of Robotics, 2022.
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.
Baisero and Amato, “Unbiased Asymmetric Reinforcement Learning under Partial Observability,” in Proceedings of the Conference on Autonomous Agents and Multiagent Systems, 2022.
Baisero and Amato, “Reconciling Rewards with Predictive State Representations,” in Proceedings of the International Joint Conference on Artificial Intelligence, 2021.
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.
Baisero and Amato, “Learning Internal State Models in Partially Observable Environments,” in (Workshop) Reinforcement Learning under Partial Observability, NeurIPS Workshop, 2018.
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.
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.