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ORCIDAleksandra Malysheva
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2020 – today
- 2022
[d1]Julien Pérolat
, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls:
Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". Zenodo, 2022
- [i6]Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls:
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR abs/2206.15378 (2022) - 2020
- [i5]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data. CoRR abs/2012.08824 (2020) - [i4]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning. CoRR abs/2012.09762 (2020) - [i3]Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman:
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box. CoRR abs/2012.09771 (2020)
2010 – 2019
- 2019
- [c4]Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman:
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box. REDUNDANCY 2019: 165-170 - [c3]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning. REDUNDANCY 2019: 171-176 - [i2]Lukasz Kidzinski, Carmichael F. Ong, Sharada Prasanna Mohanty, Jennifer L. Hicks, Sean F. Carroll, Bo Zhou, Hong-cheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaskowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy D. Watson, Marcel Salathé, Sergey Levine, Scott L. Delp:
Artificial Intelligence for Prosthetics - challenge solutions. CoRR abs/1902.02441 (2019) - 2018
- [c2]Valliappa Chockalingam, Tegg Tae Kyong Sung, Feryal M. P. Behbahani, Rishab Gargeya, Amlesh Sivanantham, Aleksandra Malysheva:
Extending World Models for Multi-Agent Reinforcement Learning in MALMÖ. AIIDE Workshops 2018 - [c1]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data. ICARCV 2018: 286-291 - [i1]Aleksandra Malysheva, Tegg Tae Kyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman:
Deep Multi-Agent Reinforcement Learning with Relevance Graphs. CoRR abs/1811.12557 (2018)
Coauthor Index
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