Amato, Christopher

40 publications

ICML 2025 Adversarial Inception Backdoor Attacks Against Reinforcement Learning Ethan Rathbun, Alina Oprea, Christopher Amato

TMLR 2025 Leveraging Fully-Observable Solutions for Improved Partially-Observable Offline Reinforcement Learning Chulabhaya Wijesundara, Andrea Baisero, Gregory David Castanon, Alan S Carlin, Robert Platt, Christopher Amato

CoRL 2024 Leveraging Mutual Information for Asymmetric Learning Under Partial Observability Hai Huu Nguyen, Long Dinh Van The, Christopher Amato, Robert Platt

NeurIPS 2024 SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents Ethan Rathbun, Christopher Amato, Alina Oprea

CoRL 2023 Equivariant Reinforcement Learning Under Partial Observability Hai Huu Nguyen, Andrea Baisero, David Klee, Dian Wang, Robert Platt, Christopher Amato

ICLR 2023 Improving Deep Policy Gradients with Value Function Search Enrico Marchesini, Christopher Amato

JAIR 2023 On Centralized Critics in Multi-Agent Reinforcement Learning Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Brett Daley, Christopher Amato

ICML 2023 Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning Brett Daley, Martha White, Christopher Amato, Marlos C. Machado

NeurIPSW 2023 Vision-and-Language Navigation in Real World Using Foundation Models Chengguang Xu, Hieu Trung Nguyen, Christopher Amato, Lawson L.S. Wong

AAAI 2022 A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato

UAI 2022 Asymmetric DQN for Partially Observable Reinforcement Learning Andrea Baisero, Brett Daley, Christopher Amato

NeurIPS 2022 Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning Yuchen Xiao, Weihao Tan, Christopher Amato

NeurIPSW 2022 Deep Transformer Q-Networks for Partially Observable Reinforcement Learning Kevin Esslinger, Robert Platt, Christopher Amato

CoRL 2022 Leveraging Fully Observable Policies for Learning Under Partial Observability Hai Huu Nguyen, Andrea Baisero, Dian Wang, Christopher Amato, Robert Platt

NeurIPS 2022 Shield Decentralization for Safe Multi-Agent Reinforcement Learning Daniel Melcer, Christopher Amato, Stavros Tripakis

IJCAI 2021 Reconciling Rewards with Predictive State Representations Andrea Baisero, Christopher Amato

CoRL 2020 Belief-Grounded Networks for Accelerated Robot Learning Under Partial Observability Hai Nguyen, Brett Daley, Xinchao Song, Christopher Amato, Robert Platt

CoRL 2020 Hierarchical Robot Navigation in Novel Environments Using Rough 2-D Maps Chengguang Xu, Christopher Amato, Lawson Wong

AAAI 2020 Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract) Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato

AAAI 2019 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

CoRL 2019 Macro-Action-Based Deep Multi-Agent Reinforcement Learning Yuchen Xiao, Joshua Hoffman, Christopher Amato

JAIR 2019 Modeling and Planning with Macro-Actions in Decentralized POMDPs Christopher Amato, George Dimitri Konidaris, Leslie Pack Kaelbling, Jonathan P. How

NeurIPS 2019 Reconciling Λ-Returns with Experience Replay Brett Daley, Christopher Amato

IJCAI 2018 Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning Christopher Amato

IJCAI 2017 COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs Madison Clark-Turner, Christopher Amato

ICML 2017 Deep Decentralized Multi-Task Multi-Agent Reinforcement Learning Under Partial Observability Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian

ICML 2017 Learning in POMDPs with Monte Carlo Tree Search Sammie Katt, Frans A. Oliehoek, Christopher Amato

AAAI 2016 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

JAIR 2016 Optimally Solving Dec-POMDPs as Continuous-State MDPs Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet

IJCAI 2015 Exploiting Separability in Multiagent Planning with Continuous-State MDPs (Extended Abstract) Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet

AAAI 2015 Scalable Planning and Learning for Multiagent POMDPs Christopher Amato, Frans A. Oliehoek

IJCAI 2015 Stick-Breaking Policy Learning in Dec-POMDPs Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How

JAIR 2013 Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs Frans A. Oliehoek, Matthijs T. J. Spaan, Christopher Amato, Shimon Whiteson

IJCAI 2013 Optimally Solving Dec-POMDPs as Continuous-State MDPs Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet

UAI 2012 Scaling up Decentralized MDPs Through Heuristic Search Jilles Steeve Dibangoye, Christopher Amato, Arnaud Doniec

IJCAI 2011 Scaling up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion Matthijs T. J. Spaan, Frans A. Oliehoek, Christopher Amato

AAAI 2010 Finite-State Controllers Based on Mealy Machines for Centralized and Decentralized POMDPs Christopher Amato, Blai Bonet, Shlomo Zilberstein

JAIR 2009 Policy Iteration for Decentralized Control of Markov Decision Processes Daniel S. Bernstein, Christopher Amato, Eric A. Hansen, Shlomo Zilberstein

UAI 2007 Optimizing Memory-Bounded Controllers for Decentralized POMDPs Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein

IJCAI 2007 Solving POMDPs Using Quadratically Constrained Linear Programs Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein