| 2026 | AAAI | LLM Collaboration with Multi-Agent Reinforcement Learning. | Shuo Liu, Zeyu Liang, Xueguang Lyu, Christopher Amato |
| 2025 | ICML | Adversarial Inception Backdoor Attacks against Reinforcement Learning. | Ethan Rathbun, Alina Oprea, Christopher Amato |
| 2024 | CoRL | Leveraging Mutual Information for Asymmetric Learning under Partial Observability. | Hai Huu Nguyen, Long Dinh Van The, Christopher Amato, Robert Platt |
| 2024 | ICRA | Robot Navigation in Unseen Environments using Coarse Maps. | Chengguang Xu, Christopher Amato, Lawson L. S. Wong |
| 2023 | CoRL | Equivariant Reinforcement Learning under Partial Observability. | Hai Nguyen, Andrea Baisero, David Klee, Dian Wang, Robert Platt, Christopher Amato |
| 2023 | ICLR | Improving Deep Policy Gradients with Value Function Search. | Enrico Marchesini, Christopher Amato |
| 2023 | ICML | Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. | Brett Daley, Martha White, Christopher Amato, Marlos C. Machado |
| 2023 | IROS | On-Robot Bayesian Reinforcement Learning for POMDPs. | Hai Nguyen, Sammie Katt, Yuchen Xiao, Christopher Amato |
| 2022 | AAAI | A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning. | Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato |
| 2022 | CoRL | Leveraging Fully Observable Policies for Learning under Partial Observability. | Hai Nguyen, Andrea Baisero, Dian Wang, Christopher Amato, Robert Platt |
| 2022 | GECCO | Safety-informed mutations for evolutionary deep reinforcement learning. | Enrico Marchesini, Christopher Amato |
| 2022 | UAI | Asymmetric DQN for partially observable reinforcement learning. | Andrea Baisero, Brett Daley, Christopher Amato |
| 2022 | WAFR | Hierarchical Reinforcement Learning Under Mixed Observability. | Hai Nguyen, Zhihan Yang, Andrea Baisero, Xiao Ma, Robert Platt, Christopher Amato |
| 2021 | IJCAI | Reconciling Rewards with Predictive State Representations. | Andrea Baisero, Christopher Amato |
| 2021 | ICRA | End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning | Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato, Taskin Padir |
| 2021 | SAC | Multi-agent reinforcement learning with directed exploration and selective memory reuse. | Shuo Jiang, Christopher Amato |
| 2020 | AAAI | Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract). | Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato |
| 2020 | CoRL | Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability. | Hai Nguyen, Brett Daley, Xinchao Song, Christopher Amato, Robert Platt |
| 2020 | CoRL | Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps. | Chengguang Xu, Christopher Amato, Lawson L. S. Wong |
| 2020 | IROS | To Ask or Not to Ask: A User Annoyance Aware Preference Elicitation Framework for Social Robots. | Balint Gucsi, Danesh S. Tarapore, William Yeoh, Christopher Amato, Long Tran-Thanh |
| 2020 | ICRA | Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net. | Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato |
| 2019 | AAAI | Learning to Teach in Cooperative Multiagent Reinforcement Learning. | Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How |
| 2019 | CoRL | Macro-Action-Based Deep Multi-Agent Reinforcement Learning. | Yuchen Xiao, Joshua Hoffman, Christopher Amato |
| 2019 | ICRA | Online Planning for Target Object Search in Clutter under Partial Observability. | Yuchen Xiao, Sammie Katt, Andreas ten Pas, Shengjian Chen, Christopher Amato |
| 2018 | IJCAI | Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning. | Christopher Amato |
| 2018 | ICRA | Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. | Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How |
| 2018 | RecSys | The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena games. | Zhengxing Chen, Truong-Huy D. Nguyen, Yuyu Xu, Christopher Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr |
| 2017 | ICML | Learning in POMDPs with Monte Carlo Tree Search. | Sammie Katt, Frans A. Oliehoek, Christopher Amato |
| 2017 | ICML | Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. | Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian |
| 2017 | IJCAI | COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs. | Madison Clark-Turner, Christopher Amato |
| 2017 | ICRA | Scalable accelerated decentralized multi-robot policy search in continuous observation spaces. | Shayegan Omidshafiei, Christopher Amato, Miao Liu, Michael Everett, Jonathan P. How, John Vian |
| 2017 | ICRA | Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations. | Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett, Brett T. Lopez, Christopher Amato, Miao Liu, Jonathan P. How, John Vian |
| 2017 | IROS | Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions. | Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei, Christopher Amato, Jonathan P. How |
| 2016 | AAAI | Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments. | Miao Liu, Christopher Amato, Emily P. Anesta, John Daniel Griffith, Jonathan P. How |
| 2016 | ICRA | Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs. | Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato, Shih-Yuan Liu, Jonathan P. How, John Vian |
| 2015 | AAAI | Scalable Planning and Learning for Multiagent POMDPs. | Christopher Amato, Frans A. Oliehoek |
| 2015 | IJCAI | Exploiting Separability in Multiagent Planning with Continuous-State MDPs (Extended Abstract). | Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, Franois Charpillet |
| 2015 | IJCAI | Stick-Breaking Policy Learning in Dec-POMDPs. | Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How |
| 2015 | ICRA | Planning for decentralized control of multiple robots under uncertainty. | Christopher Amato, George Dimitri Konidaris, Gabriel Cruz, Christopher A. Maynor, Jonathan P. How, Leslie Pack Kaelbling |
| 2015 | ICRA | Decentralized control of Partially Observable Markov Decision Processes using belief space macro-actions. | Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato, Jonathan P. How |
| 2013 | IJCAI | Optimally Solving Dec-POMDPs as Continuous-State MDPs. | Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, Franois Charpillet |
| 2012 | IAAI | Using POMDPs to Control an Accuracy-Processing Time Trade-Off in Video Surveillance. | Komal Kapoor, Christopher Amato, Nisheeth Srivastava, Paul R. Schrater |
| 2012 | UAI | Scaling Up Decentralized MDPs Through Heuristic Search. | Jilles Steeve Dibangoye, Christopher Amato, Arnaud Doniec |
| 2011 | IJCAI | Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion. | Matthijs T. J. Spaan, Frans A. Oliehoek, Christopher Amato |
| 2010 | AAAI | Finite-State Controllers Based on Mealy Machines for Centralized and Decentralized POMDPs. | Christopher Amato, Blai Bonet, Shlomo Zilberstein |
| 2007 | IJCAI | Solving POMDPs Using Quadratically Constrained Linear Programs. | Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein |
| 2007 | UAI | Optimizing Memory-Bounded Controllers for Decentralized POMDPs. | Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein |
| 2006 | ISAIM | Finding Optimal POMDP Controllers Using Quadratically Constrained Linear Programs. | Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein |