| A survey and critique of multiagent deep reinforcement learning P Hernandez-Leal, B Kartal, ME Taylor Autonomous Agents and Multi-Agent Systems 33 (6), 750-797, 2019 | 703* | 2019 |
| A survey of learning in multiagent environments: Dealing with non-stationarity P Hernandez-Leal, M Kaisers, T Baarslag, EM De Cote arXiv preprint arXiv:1707.09183, 2017 | 331 | 2017 |
| Local energy markets: Paving the path toward fully transactive energy systems F Lezama, J Soares, P Hernandez-Leal, M Kaisers, T Pinto, Z Vale IEEE Transactions on Power Systems 34 (5), 4081-4088, 2018 | 324 | 2018 |
| Multi-label classification with Bayesian network-based chain classifiers LE Sucar, C Bielza, EF Morales, P Hernandez-Leal, JH Zaragoza, ... Pattern Recognition Letters 41, 14-22, 2014 | 144 | 2014 |
| Uncertainty-aware action advising for deep reinforcement learning agents FL Da Silva, P Hernandez-Leal, B Kartal, ME Taylor Proceedings of the AAAI conference on artificial intelligence 34 (04), 5792-5799, 2020 | 81 | 2020 |
| Stress modelling and prediction in presence of scarce data A Maxhuni, P Hernandez-Leal, LE Sucar, V Osmani, EF Morales, ... Journal of biomedical informatics 63, 344-356, 2016 | 81 | 2016 |
| Agent modeling as auxiliary task for deep reinforcement learning P Hernandez-Leal, B Kartal, ME Taylor Proceedings of the AAAI conference on artificial intelligence and …, 2019 | 55 | 2019 |
| Efficiently detecting switches against non-stationary opponents P Hernandez-Leal, Y Zhan, ME Taylor, LE Sucar, E Munoz de Cote Autonomous Agents and Multi-Agent Systems 31, 767-789, 2017 | 44 | 2017 |
| Identifying and tracking switching, non-stationary opponents: A Bayesian approach P Hernandez-Leal, ME Taylor, BS Rosman, LE Sucar, E Munoz de Cote Association for the Advancement of Artificial Intelligence (AAAI), 2016 | 35 | 2016 |
| Terminal prediction as an auxiliary task for deep reinforcement learning B Kartal, P Hernandez-Leal, ME Taylor Proceedings of the AAAI Conference on Artificial Intelligence and …, 2019 | 34 | 2019 |
| Towards a fast detection of opponents in repeated stochastic games P Hernandez-Leal, M Kaisers Autonomous Agents and Multiagent Systems: AAMAS 2017 Workshops, Best Papers …, 2017 | 34 | 2017 |
| InstanceRank based on borders for instance selection P Hernandez-Leal, JA Carrasco-Ochoa, JF Martínez-Trinidad, ... Pattern Recognition 46 (1), 365-375, 2013 | 32 | 2013 |
| Learning against sequential opponents in repeated stochastic games P Hernandez-Leal, M Kaisers The 3rd Multi-disciplinary Conference on Reinforcement Learning and Decision …, 2017 | 29 | 2017 |
| Cdt: Cascading decision trees for explainable reinforcement learning Z Ding, P Hernandez-Leal, GW Ding, C Li, R Huang arXiv preprint arXiv:2011.07553, 2020 | 27 | 2020 |
| A framework for learning and planning against switching strategies in repeated games P Hernandez-Leal, E Munoz de Cote, LE Sucar Connection Science 26 (2), 103-122, 2014 | 27 | 2014 |
| Action guidance with MCTS for deep reinforcement learning B Kartal, P Hernandez-Leal, ME Taylor Proceedings of the AAAI conference on artificial intelligence and …, 2019 | 26 | 2019 |
| Learning temporal nodes Bayesian networks P Hernandez-Leal, JA Gonzalez, EF Morales, LE Sucar International Journal of Approximate Reasoning 54 (8), 956-977, 2013 | 26 | 2013 |
| A Bayesian approach for learning and tracking switching, non-stationary opponents P Hernandez-Leal, B Rosman, ME Taylor, LE Sucar, E Munoz de Cote Proceedings of the 2016 international conference on autonomous agents …, 2016 | 23 | 2016 |
| Skynet: A top deep rl agent in the inaugural pommerman team competition C Gao, P Hernandez-Leal, B Kartal, ME Taylor arXiv preprint arXiv:1905.01360, 2019 | 22 | 2019 |
| An exploration strategy for non-stationary opponents P Hernandez-Leal, Y Zhan, ME Taylor, LE Sucar, E Munoz de Cote Autonomous Agents and Multi-Agent Systems 31, 971-1002, 2017 | 22 | 2017 |
Matthew E. TaylorProfessor, University of Alberta在 ualberta.ca 的电子邮件经过验证
