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2020 – today
- 2024
[c78]Ayhan Alp Aydeniz, Enrico Marchesini, Christopher Amato, Kagan Tumer:
Entropy Seeking Constrained Multiagent Reinforcement Learning. AAMAS 2024: 2141-2143- [c77]Daniel Melcer, Christopher Amato, Stavros Tripakis:
Shield Decentralization for Safe Reinforcement Learning in General Partially Observable Multi-Agent Environments. AAMAS 2024: 2384-2386 - [c76]Chengguang Xu, Christopher Amato, Lawson L. S. Wong:
Robot Navigation in Unseen Environments using Coarse Maps. ICRA 2024: 2932-2938 - [i52]Christopher Amato:
(A Partial Survey of) Decentralized, Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2405.06161 (2024) - [i51]Ethan Rathbun, Christopher Amato, Alina Oprea:
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents. CoRR abs/2405.20539 (2024) - 2023
- [j13]Rohit Bokade
, Xiaoning Jin, Christopher Amato:
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control. IEEE Access 11: 47646-47658 (2023) - [j12]Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Brett Daley, Christopher Amato:
On Centralized Critics in Multi-Agent Reinforcement Learning. J. Artif. Intell. Res. 77: 295-354 (2023) - [c75]Enrico Marchesini, Luca Marzari, Alessandro Farinelli, Christopher Amato:
Safe Deep Reinforcement Learning by Verifying Task-Level Properties. AAMAS 2023: 1466-1475 - [c74]Hai Huu Nguyen, Andrea Baisero, David Klee, Dian Wang, Robert Platt, Christopher Amato:
Equivariant Reinforcement Learning under Partial Observability. CoRL 2023: 3309-3320 - [c73]Enrico Marchesini, Christopher Amato:
Improving Deep Policy Gradients with Value Function Search. ICLR 2023 - [c72]Brett Daley, Martha White, Christopher Amato, Marlos C. Machado:
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. ICML 2023: 6818-6835 - [c71]Hai Nguyen, Sammie Katt, Yuchen Xiao, Christopher Amato:
On-Robot Bayesian Reinforcement Learning for POMDPs. IROS 2023: 9480-9487 - [i50]Brett Daley, Martha White, Christopher Amato, Marlos C. Machado:
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. CoRR abs/2301.11321 (2023) - [i49]Enrico Marchesini, Luca Marzari, Alessandro Farinelli, Christopher Amato:
Safe Deep Reinforcement Learning by Verifying Task-Level Properties. CoRR abs/2302.10030 (2023) - [i48]Enrico Marchesini, Christopher Amato:
Improving Deep Policy Gradients with Value Function Search. CoRR abs/2302.10145 (2023) - [i47]Hai Nguyen, Sammie Katt, Yuchen Xiao, Christopher Amato:
On-Robot Bayesian Reinforcement Learning for POMDPs. CoRR abs/2307.11954 (2023) - [i46]Rohit Bokade, Xiaoning Jin, Christopher Amato:
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control. CoRR abs/2310.02435 (2023) - [i45]Chengguang Xu, Hieu T. Nguyen, Christopher Amato, Lawson L. S. Wong:
Vision and Language Navigation in the Real World via Online Visual Language Mapping. CoRR abs/2310.10822 (2023) - 2022
- [c70]Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato:
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning. AAAI 2022: 9396-9404 - [c69]Andrea Baisero, Christopher Amato:
Unbiased Asymmetric Reinforcement Learning under Partial Observability. AAMAS 2022: 44-52 - [c68]Sammie Katt, Hai Nguyen, Frans A. Oliehoek, Christopher Amato:
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs. AAMAS 2022: 723-731 - [c67]Hai Huu Nguyen, Andrea Baisero, Dian Wang, Christopher Amato, Robert Platt:
Leveraging Fully Observable Policies for Learning under Partial Observability. CoRL 2022: 1673-1683 - [c66]Enrico Marchesini, Christopher Amato:
Safety-informed mutations for evolutionary deep reinforcement learning. GECCO Companion 2022: 1966-1970 - [c65]Daniel Melcer, Christopher Amato, Stavros Tripakis:
Shield Decentralization for Safe Multi-Agent Reinforcement Learning. NeurIPS 2022 - [c64]Yuchen Xiao, Weihao Tan, Christopher Amato:
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning. NeurIPS 2022 - [c63]Andrea Baisero, Brett Daley, Christopher Amato:
Asymmetric DQN for partially observable reinforcement learning. UAI 2022: 107-117 - [c62]Hai Huu Nguyen, Zhihan Yang, Andrea Baisero, Xiao Ma, Robert Platt, Christopher Amato:
Hierarchical Reinforcement Learning Under Mixed Observability. WAFR 2022: 188-204 - [e1]Jie Chen, Jérôme Lang, Christopher Amato, Dengji Zhao:
Distributed Artificial Intelligence - Third International Conference, DAI 2021, Shanghai, China, December 17-18, 2021, Proceedings. Lecture Notes in Computer Science 13170, Springer 2022, ISBN 978-3-030-94661-6 [contents] - [i44]Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato:
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning. CoRR abs/2201.01221 (2022) - [i43]Sammie Katt, Hai Nguyen, Frans A. Oliehoek, Christopher Amato:
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs. CoRR abs/2202.08884 (2022) - [i42]Hai Huu Nguyen, Zhihan Yang, Andrea Baisero, Xiao Ma, Robert Platt, Christopher Amato:
Hierarchical Reinforcement Learning under Mixed Observability. CoRR abs/2204.00898 (2022) - [i41]Kevin Esslinger, Robert Platt, Christopher Amato:
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning. CoRR abs/2206.01078 (2022) - [i40]Yuchen Xiao, Weihao Tan, Christopher Amato:
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning. CoRR abs/2209.10113 (2022) - [i39]Hai Huu Nguyen, Andrea Baisero, Dian Wang, Christopher Amato, Robert Platt:
Leveraging Fully Observable Policies for Learning under Partial Observability. CoRR abs/2211.01991 (2022) - 2021
- [c61]Ingy Elsayed-Aly, Suda Bharadwaj, Christopher Amato, Rüdiger Ehlers, Ufuk Topcu, Lu Feng:
Safe Multi-Agent Reinforcement Learning via Shielding. AAMAS 2021: 483-491 - [c60]Xueguang Lyu, Yuchen Xiao, Brett Daley, Christopher Amato:
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning. AAMAS 2021: 844-852 - [c59]Brett Daley, Cameron Hickert, Christopher Amato:
Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning. AAMAS 2021: 1486-1488 - [c58]Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato, Taskin Padir:
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning*. ICRA 2021: 2768-2774 - [c57]Andrea Baisero, Christopher Amato:
Reconciling Rewards with Predictive State Representations. IJCAI 2021: 2170-2176 - [c56]Yuchen Xiao, Xueguang Lyu, Christopher Amato:
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning. MRS 2021: 155-163 - [c55]Shuo Jiang, Christopher Amato:
Multi-agent reinforcement learning with directed exploration and selective memory reuse. SAC 2021: 777-784 - [i38]Ingy Elsayed-Aly, Suda Bharadwaj, Christopher Amato, Rüdiger Ehlers, Ufuk Topcu, Lu Feng:
Safe Multi-Agent Reinforcement Learning via Shielding. CoRR abs/2101.11196 (2021) - [i37]Xueguang Lyu, Yuchen Xiao, Brett Daley, Christopher Amato:
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning. CoRR abs/2102.04402 (2021) - [i36]Brett Daley, Cameron Hickert, Christopher Amato:
Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning. CoRR abs/2102.11319 (2021) - [i35]Roi Yehoshua, Juan Heredia Juesas, Yushu Wu, Christopher Amato, Jose A. Martinez-Lorenzo:
Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones. CoRR abs/2103.09520 (2021) - [i34]Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato, Taskin Padir:
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning. CoRR abs/2104.12842 (2021) - [i33]Andrea Baisero, Christopher Amato:
Unbiased Asymmetric Actor-Critic for Partially Observable Reinforcement Learning. CoRR abs/2105.11674 (2021) - [i32]Chengguang Xu, Christopher Amato, Lawson L. S. Wong:
Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps. CoRR abs/2106.03665 (2021) - [i31]Andrea Baisero, Christopher Amato:
Reconciling Rewards with Predictive State Representations. CoRR abs/2106.03926 (2021) - [i30]Brett Daley, Christopher Amato:
Investigating Alternatives to the Root Mean Square for Adaptive Gradient Methods. CoRR abs/2106.05449 (2021) - [i29]Yuchen Xiao, Xueguang Lyu, Christopher Amato:
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning. CoRR abs/2110.08642 (2021) - [i28]Brett Daley, Christopher Amato:
Human-Level Control without Server-Grade Hardware. CoRR abs/2111.01264 (2021) - [i27]Brett Daley, Christopher Amato:
Virtual Replay Cache. CoRR abs/2112.03421 (2021) - [i26]Brett Daley, Christopher Amato:
Improving the Efficiency of Off-Policy Reinforcement Learning by Accounting for Past Decisions. CoRR abs/2112.12281 (2021) - 2020
- [j11]Nikolay Atanasov, Chris Amato:
Special Issue on the 2018 Robotics: Science and Systems Conference. Int. J. Robotics Res. 39(2-3) (2020) - [c54]Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato:
Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract). AAAI 2020: 13965-13966 - [c53]Xueguang Lyu, Christopher Amato:
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning. AAMAS 2020: 798-806 - [c52]Andrea Baisero, Christopher Amato:
Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning. AAMAS 2020: 1762-1764 - [c51]Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato, Taskin Padir:
Towards End-to-End Control of a Robot Prosthetic Hand via Reinforcement Learning. BioRob 2020: 641-647 - [c50]Hai Nguyen, Brett Daley, Xinchao Song, Christopher Amato, Robert Platt:
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability. CoRL 2020: 1640-1653 - [c49]Chengguang Xu, Christopher Amato, Lawson L. S. Wong:
Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps. CoRL 2020: 1971-1991 - [c48]Roi Yehoshua, Christopher Amato:
Hybrid Independent Learning in Cooperative Markov Games. DAI 2020: 69-84 - [c47]Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato:
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net. ICRA 2020: 10695-10701 - [c46]Balint Gucsi, Danesh S. Tarapore, William Yeoh, Christopher Amato, Long Tran-Thanh:
To Ask or Not to Ask: A User Annoyance Aware Preference Elicitation Framework for Social Robots. IROS 2020: 7935-7940 - [i25]Yuchen Xiao, Joshua Hoffman, Christopher Amato:
Macro-Action-Based Deep Multi-Agent Reinforcement Learning. CoRR abs/2004.08646 (2020) - [i24]Brett Daley, Christopher Amato:
Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties. CoRR abs/2010.01356 (2020) - [i23]Hai Nguyen, Brett Daley, Xinchao Song, Christopher Amato, Robert Platt:
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability. CoRR abs/2010.09170 (2020)
2010 – 2019
- 2019
- [j10]Christopher Amato, John P. Dickerson:
AAAI/ACM SIGAI job fair 2019: a retrospective. AI Matters 5(1): 5-6 (2019) - [j9]Christopher Amato, George Dimitri Konidaris, Leslie Pack Kaelbling, Jonathan P. How:
Modeling and Planning with Macro-Actions in Decentralized POMDPs. J. Artif. Intell. Res. 64: 817-859 (2019) - [c45]Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How:
Learning to Teach in Cooperative Multiagent Reinforcement Learning. AAAI 2019: 6128-6136 - [c44]Sammie Katt, Frans A. Oliehoek, Christopher Amato:
Bayesian Reinforcement Learning in Factored POMDPs. AAMAS 2019: 7-15 - [c43]Sammie Katt, Frans A. Oliehoek, Chris Amato:
Bayesian RL in Factored POMDPs. BNAIC/BENELEARN 2019 - [c42]Yuchen Xiao, Joshua Hoffman, Christopher Amato:
Macro-Action-Based Deep Multi-Agent Reinforcement Learning. CoRL 2019: 1146-1161 - [c41]Yuchen Xiao, Sammie Katt, Andreas ten Pas, Shengjian Chen, Christopher Amato:
Online Planning for Target Object Search in Clutter under Partial Observability. ICRA 2019: 8241-8247 - [c40]Brett Daley, Christopher Amato:
Reconciling λ-Returns with Experience Replay. NeurIPS 2019: 1131-1140 - [i22]Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato:
Multi-Robot Deep Reinforcement Learning with Macro-Actions. CoRR abs/1909.08776 (2019) - [i21]Christopher Amato, Andrea Baisero:
Active Goal Recognition. CoRR abs/1909.11173 (2019) - 2018
- [j8]Christopher Amato, Haitham Bou-Ammar, Elizabeth F. Churchill, Erez Karpas, Takashi Kido, Mike Kuniavsky, William F. Lawless, Francesca Rossi, Frans A. Oliehoek, Stephen Russell, Keiki Takadama, Siddharth Srivastava, Karl Tuyls, Philip van Allen, Kristen Brent Venable, Peter Vrancx, Shiqi Zhang:
Reports on the 2018 AAAI Spring Symposium Series. AI Mag. 39(4): 29-35 (2018) - [c39]Zhengxing Chen, Christopher Amato, Truong-Huy D. Nguyen, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr:
Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games. CIG 2018: 1-8 - [c38]Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How:
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. ICRA 2018: 6373-6380 - [c37]Christopher Amato:
Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning. IJCAI 2018: 5662-5666 - [c36]Zhengxing Chen, Truong-Huy D. Nguyen, Yuyu Xu, Christopher Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr:
The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena games. RecSys 2018: 200-208 - [i20]Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How:
Learning to Teach in Cooperative Multiagent Reinforcement Learning. CoRR abs/1805.07830 (2018) - [i19]Sammie Katt, Frans A. Oliehoek, Christopher Amato:
Learning in POMDPs with Monte Carlo Tree Search. CoRR abs/1806.05631 (2018) - [i18]Zhengxing Chen, Chris Amato, Truong-Huy D. Nguyen, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr:
Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games. CoRR abs/1806.09771 (2018) - [i17]Zhengxing Chen, Truong-Huy D. Nguyen, Yuyu Xu, Chris Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr:
The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games. CoRR abs/1806.10130 (2018) - [i16]Brett Daley, Christopher Amato:
Efficient Eligibility Traces for Deep Reinforcement Learning. CoRR abs/1810.09967 (2018) - [i15]Sammie Katt, Frans A. Oliehoek, Christopher Amato:
Bayesian Reinforcement Learning in Factored POMDPs. CoRR abs/1811.05612 (2018) - [i14]Xueguang Lyu, Christopher Amato:
On Improving Decentralized Hysteretic Deep Reinforcement Learning. CoRR abs/1812.06319 (2018) - 2017
- [j7]Shayegan Omidshafiei, Ali-Akbar Agha-Mohammadi, Christopher Amato, Shih-Yuan Liu, Jonathan P. How, John Vian:
Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions. Int. J. Robotics Res. 36(2): 231-258 (2017) - [c35]Sammie Katt, Frans A. Oliehoek, Christopher Amato:
Learning in POMDPs with Monte Carlo Tree Search. ICML 2017: 1819-1827 - [c34]Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian:
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. ICML 2017: 2681-2690 - [c33]Shayegan Omidshafiei, Christopher Amato, Miao Liu, Michael Everett, Jonathan P. How, John Vian:
Scalable accelerated decentralized multi-robot policy search in continuous observation spaces. ICRA 2017: 863-870 - [c32]Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett, Brett Thomas Lopez, Christopher Amato, Miao Liu, Jonathan P. How, John Vian:
Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations. ICRA 2017: 871-878 - [c31]Madison Clark-Turner, Christopher Amato:
COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs. IJCAI 2017: 4573-4579 - [c30]Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei, Christopher Amato, Jonathan P. How:
Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions. IROS 2017: 1853-1860 - [i13]Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett, Brett Thomas Lopez, Christopher Amato, Miao Liu, Jonathan P. How, John Vian:
Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations. CoRR abs/1703.05623 (2017) - [i12]Shayegan Omidshafiei, Christopher Amato, Miao Liu, Michael Everett, Jonathan P. How, John Vian:
Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces. CoRR abs/1703.05626 (2017) - [i11]Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian:
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability. CoRR abs/1703.06182 (2017) - [i10]Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei, Christopher Amato, Jonathan P. How:
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions. CoRR abs/1707.07399 (2017) - [i9]Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How:
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. CoRR abs/1710.06525 (2017) - 2016
- [b1]Frans A. Oliehoek, Christopher Amato:
A Concise Introduction to Decentralized POMDPs. Springer Briefs in Intelligent Systems, Springer 2016, ISBN 978-3-319-28927-4, pp. 1-116 - [j6]Christopher Amato, Ofra Amir, Joanna Bryson, Barbara J. Grosz, Bipin Indurkhya, Emre Kiciman, Takashi Kido, William F. Lawless, Miao Liu, Braden McDorman, Ross Mead, Frans A. Oliehoek, Andrew Specian, Georgi Stojanov, Keiki Takadama:
Reports of the AAAI 2016 Spring Symposium Series. AI Mag. 37(4): 83-88 (2016) - [j5]Christopher Amato, George Dimitri Konidaris, Ariel Anders, Gabriel Cruz, Jonathan P. How, Leslie Pack Kaelbling:
Policy search for multi-robot coordination under uncertainty. Int. J. Robotics Res. 35(14): 1760-1778 (2016) - [j4]Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet:
Optimally Solving Dec-POMDPs as Continuous-State MDPs. J. Artif. Intell. Res. 55: 443-497 (2016) - [c29]Miao Liu, Christopher Amato, Emily P. Anesta, John Daniel Griffith, Jonathan P. How:
Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments. AAAI 2016: 2523-2529 - [c28]Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato, Shih-Yuan Liu, Jonathan P. How, John Vian:
Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs. ICRA 2016: 5395-5402 - 2015
- [c27]Christopher Amato, Frans A. Oliehoek:
Scalable Planning and Learning for Multiagent POMDPs. AAAI 2015: 1995-2002 - [c26]Christopher Amato, George Dimitri Konidaris, Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Jonathan P. How, Leslie Pack Kaelbling:
Probabilistic Planning for Decentralized Multi-Robot Systems. AAAI Fall Symposia 2015: 10-12 - [c25]Christopher Amato, George Dimitri Konidaris, Gabriel Cruz, Christopher A. Maynor, Jonathan P. How, Leslie Pack Kaelbling:
Planning for decentralized control of multiple robots under uncertainty. ICRA 2015: 1241-1248 - [c24]Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato, Jonathan P. How:
Decentralized control of Partially Observable Markov Decision Processes using belief space macro-actions. ICRA 2015: 5962-5969 - [c23]Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How:
Stick-Breaking Policy Learning in Dec-POMDPs. IJCAI 2015: 2011-2018 - [c22]Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet:
Exploiting Separability in Multiagent Planning with Continuous-State MDPs (Extended Abstract). IJCAI 2015: 4254-4260 - [c21]Christopher Amato, George Dimitri Konidaris, Ariel Anders, Gabriel Cruz, Jonathan P. How, Leslie Pack Kaelbling:
Policy Search for Multi-Robot Coordination under Uncertainty. Robotics: Science and Systems 2015 - [i8]Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato, Jonathan P. How:
Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions. CoRR abs/1502.06030 (2015) - [i7]Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How:
Stick-Breaking Policy Learning in Dec-POMDPs. CoRR abs/1505.00274 (2015) - 2014
- [c20]Christopher Amato, George Dimitri Konidaris, Leslie Pack Kaelbling:
Planning with macro-actions in decentralized POMDPs. AAMAS 2014: 1273-1280 - [c19]Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet:
Exploiting separability in multiagent planning with continuous-state MDPs. AAMAS 2014: 1281-1288 - [i6]Daniel S. Bernstein, Christopher Amato, Eric A. Hansen, Shlomo Zilberstein:
Policy Iteration for Decentralized Control of Markov Decision Processes. CoRR abs/1401.3460 (2014) - [i5]