Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Vezhnevets, Alexander Sasha"'
Autor:
Vezhnevets, Alexander Sasha, Agapiou, John P., Aharon, Avia, Ziv, Ron, Matyas, Jayd, Duéñez-Guzmán, Edgar A., Cunningham, William A., Osindero, Simon, Karmon, Danny, Leibo, Joel Z.
Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM
Externí odkaz:
http://arxiv.org/abs/2312.03664
Autor:
Sunehag, Peter, Vezhnevets, Alexander Sasha, Duéñez-Guzmán, Edgar, Mordach, Igor, Leibo, Joel Z.
Many environments contain numerous available niches of variable value, each associated with a different local optimum in the space of behaviors (policy space). In such situations it is often difficult to design a learning process capable of evading d
Externí odkaz:
http://arxiv.org/abs/2302.01180
Autor:
Agapiou, John P., Vezhnevets, Alexander Sasha, Duéñez-Guzmán, Edgar A., Matyas, Jayd, Mao, Yiran, Sunehag, Peter, Köster, Raphael, Madhushani, Udari, Kopparapu, Kavya, Comanescu, Ramona, Strouse, DJ, Johanson, Michael B., Singh, Sukhdeep, Haas, Julia, Mordatch, Igor, Mobbs, Dean, Leibo, Joel Z.
Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Me
Externí odkaz:
http://arxiv.org/abs/2211.13746
Autor:
Kopparapu, Kavya, Duéñez-Guzmán, Edgar A., Matyas, Jayd, Vezhnevets, Alexander Sasha, Agapiou, John P., McKee, Kevin R., Everett, Richard, Marecki, Janusz, Leibo, Joel Z., Graepel, Thore
A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate. This is particularly critical in situations when other agents have hidden, possibly misa
Externí odkaz:
http://arxiv.org/abs/2201.01816
Autor:
Duéñez-Guzmán, Edgar A., McKee, Kevin R., Mao, Yiran, Coppin, Ben, Chiappa, Silvia, Vezhnevets, Alexander Sasha, Bakker, Michiel A., Bachrach, Yoram, Sadedin, Suzanne, Isaac, William, Tuyls, Karl, Leibo, Joel Z.
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One primary example is \textit{statistical discrimination} -- selecting soci
Externí odkaz:
http://arxiv.org/abs/2110.11404
Autor:
Leibo, Joel Z., Duéñez-Guzmán, Edgar, Vezhnevets, Alexander Sasha, Agapiou, John P., Sunehag, Peter, Koster, Raphael, Matyas, Jayd, Beattie, Charles, Mordatch, Igor, Graepel, Thore
Publikováno v:
In International Conference on Machine Learning 2021 (pp. 6187-6199). PMLR
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks). Our contribution, Melting Pot, is a MARL evaluation suite th
Externí odkaz:
http://arxiv.org/abs/2107.06857
Autor:
Vinitsky, Eugene, Köster, Raphael, Agapiou, John P., Duéñez-Guzmán, Edgar, Vezhnevets, Alexander Sasha, Leibo, Joel Z.
Society is characterized by the presence of a variety of social norms: collective patterns of sanctioning that can prevent miscoordination and free-riding. Inspired by this, we aim to construct learning dynamics where potentially beneficial social no
Externí odkaz:
http://arxiv.org/abs/2106.09012
Publikováno v:
International Conference on Machine Learning 2020
This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training. We propose two new games with concealed information and complex, non-transiti
Externí odkaz:
http://arxiv.org/abs/1906.01470
Autor:
Vinyals, Oriol, Ewalds, Timo, Bartunov, Sergey, Georgiev, Petko, Vezhnevets, Alexander Sasha, Yeo, Michelle, Makhzani, Alireza, Küttler, Heinrich, Agapiou, John, Schrittwieser, Julian, Quan, John, Gaffney, Stephen, Petersen, Stig, Simonyan, Karen, Schaul, Tom, van Hasselt, Hado, Silver, David, Lillicrap, Timothy, Calderone, Kevin, Keet, Paul, Brunasso, Anthony, Lawrence, David, Ekermo, Anders, Repp, Jacob, Tsing, Rodney
This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems
Externí odkaz:
http://arxiv.org/abs/1708.04782
Autor:
Vezhnevets, Alexander Sasha, Osindero, Simon, Schaul, Tom, Heess, Nicolas, Jaderberg, Max, Silver, David, Kavukcuoglu, Koray
We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learn
Externí odkaz:
http://arxiv.org/abs/1703.01161