no code implementations • 16 Oct 2023 • Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, William Spearman, Ian Graham, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess, Michael Bowling, Demis Hassabis, Karl Tuyls
The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC.
no code implementations • 31 May 2022 • Daniel Hernandez, Hendrik Baier, Michael Kaisers
Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies).
1 code implementation • 27 Jan 2022 • Jinke He, Miguel Suau, Hendrik Baier, Michael Kaisers, Frans A. Oliehoek
To plan reliably and efficiently while the approximate simulator is learning, we develop a method that adaptively decides which simulator to use for every simulation, based on a statistic that measures the accuracy of the approximate simulator.
no code implementations • 26 Nov 2019 • Shantanu Chakraborty, Tim Baarslag, Michael Kaisers
This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences.
no code implementations • 23 Jan 2019 • Richard Klima, Daan Bloembergen, Michael Kaisers, Karl Tuyls
We prove convergence of the operator to the optimal robust Q-function with respect to the model using the theory of Generalized Markov Decision Processes.
no code implementations • 28 Jul 2018 • Shantanu Chakraborty, Tim Baarslag, Michael Kaisers
This paper presents an automated peer-to-peer (P2P) negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative (REC) considering heterogeneous prosumer preferences.
Multiagent Systems
no code implementations • 28 Jul 2017 • Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, Enrique Munoz de Cote
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves.