Search Results - "Tuyls, Karl"

Evolutionary Dynamics of Multi-Agent Learning: A Survey by Bloembergen, Daan, Tuyls, Karl, Hennes, Daniel, Kaisers, Michael

“…The interaction of multiple autonomous agents gives rise to highly dynamic and nondeterministic environments, contributing to the complexity in applications…”
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Navigating the landscape of multiplayer games by Omidshafiei, Shayegan, Tuyls, Karl, Czarnecki, Wojciech M., Santos, Francisco C., Rowland, Mark, Connor, Jerome, Hennes, Daniel, Muller, Paul, Pérolat, Julien, Vylder, Bart De, Gruslys, Audrunas, Munos, Rémi

“…Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence…”
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α-Rank: Multi-Agent Evaluation by Evolution by Omidshafiei, Shayegan, Papadimitriou, Christos, Piliouras, Georgios, Tuyls, Karl, Rowland, Mark, Lespiau, Jean-Baptiste, Czarnecki, Wojciech M., Lanctot, Marc, Perolat, Julien, Munos, Remi

“…We introduce α - Rank , a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions,…”
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Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone by McGuire, Kimberly, de Croon, Guido, De Wagter, Christophe, Tuyls, Karl, Kappen, Hilbert

“…Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware…”
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Multiagent off-screen behavior prediction in football by Omidshafiei, Shayegan, Hennes, Daniel, Garnelo, Marta, Wang, Zhe, Recasens, Adria, Tarassov, Eugene, Yang, Yi, Elie, Romuald, Connor, Jerome T., Muller, Paul, Mackraz, Natalie, Cao, Kris, Moreno, Pol, Sprechmann, Pablo, Hassabis, Demis, Graham, Ian, Spearman, William, Heess, Nicolas, Tuyls, Karl

“…In multiagent worlds, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions,…”
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Game Plan: What AI can do for Football, and What Football can do for AI by Tuyls, Karl, Omidshafiei, Shayegan, Muller, Paul, Wang, Zhe, Connor, Jerome, Hennes, Daniel, Graham, Ian, Spearman, William, Waskett, Tim, Steel, Dafydd, Luc, Pauline, Recasens, Adria, Galashov, Alexandre, Thornton, Gregory, Elie, Romuald, Sprechmann, Pablo, Moreno, Pol, Cao, Kris, Garnelo, Marta, Dutta, Praneet, Valko, Michal, Heess, Nicolas, Bridgland, Alex, Pérolat, Julien, De Vylder, Bart, Eslami, S. M. Ali, Rowland, Mark, Jaegle, Andrew, Munos, Remi, Back, Trevor, Ahamed, Razia, Bouton, Simon, Beauguerlange, Nathalie, Broshear, Jackson, Graepel, Thore, Hassabis, Demis

“…The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports,…”
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Bounds and dynamics for empirical game theoretic analysis by Tuyls, Karl, Perolat, Julien, Lanctot, Marc, Hughes, Edward, Everett, Richard, Leibo, Joel Z., Szepesvári, Csaba, Graepel, Thore

“…This paper provides several theoretical results for empirical game theory. Specifically, we introduce bounds for empirical game theoretical analysis of complex…”
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TacticAI: an AI assistant for football tactics by Wang, Zhe, Veličković, Petar, Hennes, Daniel, Tomašev, Nenad, Prince, Laurel, Kaisers, Michael, Bachrach, Yoram, Elie, Romuald, Wenliang, Li Kevin, Piccinini, Federico, Spearman, William, Graham, Ian, Connor, Jerome, Yang, Yi, Recasens, Adrià, Khan, Mina, Beauguerlange, Nathalie, Sprechmann, Pablo, Moreno, Pol, Heess, Nicolas, Bowling, Michael, Hassabis, Demis, Tuyls, Karl

“…Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so…”
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Factored four way conditional restricted Boltzmann machines for activity recognition by Mocanu, Decebal Constantin, Bou Ammar, Haitham, Lowet, Dietwig, Driessens, Kurt, Liotta, Antonio, Weiss, Gerhard, Tuyls, Karl

“…•This paper proposes a new learning algorithm for human activity recognition.•Its name is factored four way conditional restricted Boltzmann machine…”
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Improved deep reinforcement learning for robotics through distribution-based experience retention by de Bruin, Tim, Kober, Jens, Tuyls, Karl, Babuska, Robert

“…Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience…”
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Space Debris Removal: Learning to Cooperate and the Price of Anarchy by Klima, Richard, Bloembergen, Daan, Savani, Rahul, Tuyls, Karl, Wittig, Alexander, Sapera, Andrei, Izzo, Dario

“…In this paper we study space debris removal from a game-theoretic perspective. In particular we focus on the question whether and how self-interested agents…”
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Space debris removal: A game theoretic analysis by Klima, Richard, Bloembergen, Daan, Savani, Rahul, Tuyls, Karl, Hennes, Daniel, Izzo, Dario

“…We analyse active space debris removal efforts from a strategic, game-theoretical perspective. Space debris is non-manoeuvrable, human-made objects orbiting…”
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Exploring selfish reinforcement learning in repeated games with stochastic rewards by Verbeeck, Katja, Nowé, Ann, Parent, Johan, Tuyls, Karl

“…In this paper we introduce a new multi-agent reinforcement learning algorithm, called exploring selfish reinforcement learning (ESRL). ESRL allows agents to…”
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Multi robot collision avoidance in a shared workspace by Claes, Daniel, Tuyls, Karl

“…This paper presents a decentralised human-aware navigation algorithm for shared human–robot work-spaces based on the velocity obstacles paradigm. By extending…”
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What evolutionary game theory tells us about multiagent learning by Tuyls, Karl, Parsons, Simon

“…This paper discusses If multi-agent learning is the answer, what is the question? [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer,…”
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Integrating State Representation Learning Into Deep Reinforcement Learning by de Bruin, Tim, Kober, Jens, Tuyls, Karl, Babuska, Robert

“…Most deep reinforcement learning techniques are unsuitable for robotics, as they require too much interaction time to learn useful, general control policies…”
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Mastering the game of Stratego with model-free multiagent reinforcement learning by Perolat, Julien, De Vylder, Bart, Hennes, Daniel, Tarassov, Eugene, Strub, Florian, de Boer, Vincent, Muller, Paul, Connor, Jerome T., Burch, Neil, Anthony, Thomas, McAleer, Stephen, Elie, Romuald, Cen, Sarah H., Wang, Zhe, Gruslys, Audrunas, Malysheva, Aleksandra, Khan, Mina, Ozair, Sherjil, Timbers, Finbarr, Pohlen, Toby, Eccles, Tom, Rowland, Mark, Lanctot, Marc, Lespiau, Jean-Baptiste, Piot, Bilal, Omidshafiei, Shayegan, Lockhart, Edward, Sifre, Laurent, Beauguerlange, Nathalie, Munos, Remi, Silver, David, Singh, Satinder, Hassabis, Demis, Tuyls, Karl

“…We introduce DeepNash, an autonomous agent that plays the imperfect information game Stratego at a human expert level. Stratego is one of the few iconic board…”
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Symmetric Decomposition of Asymmetric Games by Tuyls, Karl, Pérolat, Julien, Lanctot, Marc, Ostrovski, Georg, Savani, Rahul, Leibo, Joel Z, Ord, Toby, Graepel, Thore, Legg, Shane

“…We introduce new theoretical insights into two-population asymmetric games allowing for an elegant symmetric decomposition into two single population symmetric…”
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Evolutionary Dynamics and Phi-Regret Minimization in Games by Piliouras, Georgios, Rowland, Mark, Omidshafiei, Shayegan, Elie, Romuald, Hennes, Daniel, Connor, Jerome, Tuyls, Karl

“…Regret has been established as a foundational concept in online learning, and likewise has important applications in the analysis of learning dynamics in…”
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SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes by Zhang, Chengwei, Li, Xiaohong, Hao, Jianye, Chen, Siqi, Tuyls, Karl, Xue, Wanli, Feng, Zhiyong

“…In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment…”
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