Artigos com autorizações de acesso público - Christopher AmatoSaiba mais
Disponíveis em algum local: 29
Deep Decentralized Multi-task Multi-Agent RL under Partial Observability
S Omidshafiei, J Pazis, C Amato, JP How, J Vian
International Conference on Machine Learning, 2681-2690, 2017
Autorizações: US Department of Defense
Policy search for multi-robot coordination under uncertainty
C Amato, G Konidaris, A Anders, G Cruz, JP How, LP Kaelbling
The International Journal of Robotics Research 35 (14), 1760-1778, 2016
Autorizações: US National Science Foundation
Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions
S Omidshafiei, AA Agha–Mohammadi, C Amato, SY Liu, JP How, J Vian
The International Journal of Robotics Research 36 (2), 231-258, 2017
Autorizações: US Department of Defense
Learning in POMDPs with Monte Carlo Tree Search
S Katt, FA Oliehoek, C Amato
ICML, 2017
Autorizações: US National Science Foundation, Netherlands Organisation for Scientific Research
Online planning for target object search in clutter under partial observability
Y Xiao, S Katt, A ten Pas, S Chen, C Amato
2019 International Conference on Robotics and Automation (ICRA), 8241-8247, 2019
Autorizações: US Department of Defense
The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena games
Z Chen, THD Nguyen, Y Xu, C Amato, S Cooper, Y Sun, MS El-Nasr
Proceedings of the 12th ACM Conference on Recommender Systems, 200-208, 2018
Autorizações: US National Science Foundation
Reconciling λ-returns with experience replay
B Daley, C Amato
Advances in Neural Information Processing Systems 32, 2019
Autorizações: US National Science Foundation
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net
Y Xiao, J Hoffman, T Xia, C Amato
ICRA, 2020
Autorizações: US National Science Foundation, US Department of Defense
Macro-Action-Based Deep Multi-Agent Reinforcement Learning
Y Xiao, J Hoffman, C Amato
Conference on Robot Learning (CoRL), 2019
Autorizações: US National Science Foundation, US Department of Defense
Learning for decentralized control of multiagent systems in large, partially-observable stochastic environments
M Liu, C Amato, E Anesta, J Griffith, J How
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
Autorizações: US National Science Foundation
Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning.
C Amato
IJCAI, 5662-5666, 2018
Autorizações: US National Science Foundation, US Department of Defense
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Y Xiao, W Tan, C Amato
NeurIPS, 2022
Autorizações: US National Science Foundation, US Department of Defense
Multi-agent reinforcement learning with directed exploration and selective memory reuse
S Jiang, C Amato
Proceedings of the 36th annual ACM symposium on applied computing, 777-784, 2021
Autorizações: US Department of Defense
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
X Lyu, A Baisero, Y Xiao, C Amato
AAAI, 2022
Autorizações: US National Science Foundation, US Department of Defense
Multi-agent reinforcement learning based on representational communication for large-scale traffic signal control
R Bokade, X Jin, C Amato
IEEE Access 11, 47646-47658, 2023
Autorizações: US National Science Foundation
On centralized critics in multi-agent reinforcement learning
X Lyu, A Baisero, Y Xiao, B Daley, C Amato
Journal of Artificial Intelligence Research 77, 295-354, 2023
Autorizações: US National Science Foundation, US Department of Defense
Shield decentralization for safe multi-agent reinforcement learning
D Melcer, C Amato, S Tripakis
Advances in Neural Information Processing Systems 35, 13367-13379, 2022
Autorizações: US National Science Foundation, US Department of Defense
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
TN Hoang, Y Xiao, K Sivakumar, C Amato, J How
ICRA, 2018
Autorizações: US Department of Defense
Asymmetric DQN for partially observable reinforcement learning
A Baisero, B Daley, C Amato
Uncertainty in Artificial Intelligence, 107-117, 2022
Autorizações: US National Science Foundation
Equivariant reinforcement learning under partial observability
HH Nguyen, A Baisero, D Klee, D Wang, R Platt, C Amato
Conference on Robot Learning, 3309-3320, 2023
Autorizações: US National Science Foundation, US Department of Defense
As informações de publicação e financiamento são determinadas automaticamente por um programa informático.