Multi-agent Learning Dynamics: A Survey. - H. Jaap van den Herik; Daniel Hennes; Michael Kaisers; Karl Tuyls; Katja Verbeeck (CIA, 2007)

Formalizing Multi-state Learning Dynamics. - Daniel Hennes; Karl Tuyls; Matthias Rauterberg (IAT, 2008)

State-coupled replicator dynamics. - Daniel Hennes; Karl Tuyls; Matthias Rauterberg (AAMAS (2), 2009)

Bee-inspired foraging in an embodied swarm. - Sjriek Alers; Daan Bloembergen; Daniel Hennes; Steven de Jong; Michael Kaisers; Nyree Lemmens; Karl Tuyls; Gerhard Weiss 001 (AAMAS, 2011)

Metastrategies in the Colored Trails game. - Steven de Jong; Daniel Hennes; Karl Tuyls; Ya'akov (Kobi) Gal (AAMAS, 2011)

Hierarchies of octrees for efficient 3D mapping. - Kai M. Wurm; Daniel Hennes; Dirk Holz; Radu Bogdan Rusu; Cyrill Stachniss; Kurt Konolige; Wolfram Burgard (IROS, 2011)

MITRO: an augmented mobile telepresence robot with assisted control (demonstration). - Sjriek Alers; Daan Bloembergen; Max Bügler; Daniel Hennes; Karl Tuyls (AAMAS, 2012)

CALU: collision avoidance with localization uncertainty (demonstration). - Daniel Claes; Daniel Hennes; Wim Meeussen; Karl Tuyls (AAMAS, 2012)

Multi-robot collision avoidance with localization uncertainty. - Daniel Hennes; Daniel Claes; Wim Meeussen; Karl Tuyls (AAMAS, 2012)

Evolutionary advantage of foresight in markets. - Daniel Hennes; Daan Bloembergen; Michael Kaisers; Karl Tuyls; Simon Parsons (GECCO, 2012)

Collision avoidance under bounded localization uncertainty. - Daniel Claes; Daniel Hennes; Karl Tuyls; Wim Meeussen (IROS, 2012)

Telepresence Robots as a Research Platform for AI. - Sjriek Alers; Daan Bloembergen; Daniel Claes; Joscha-David Fossel; Daniel Hennes; Karl Tuyls (AAAI Spring Symposium: Designing Intelligent Robots, 2013)

OctoSLAM: a 3D mapping approach to situational awareness of unmanned aerial vehicles. - Joscha-David Fossel; Daniel Hennes; Sjriek Alers; Daniel Claes; Karl Tuyls (AAMAS, 2013)

Development of an Autonomous RC-car. - Daniel Claes; Joscha-David Fossel; Bastian Broecker; Daniel Hennes; Karl Tuyls (ICIRA (2), 2013)

How to Win RoboCup@Work? - The Swarmlab@Work Approach Revealed. - Sjriek Alers; Daniel Claes; Joscha-David Fossel; Daniel Hennes; Karl Tuyls; Gerhard Weiss 001 (RoboCup, 2013)

Applied robotics: precision placement in RoboCup@Work. - Sjriek Alers; Daniel Claes; Joscha-David Fossel; Daniel Hennes; Karl Tuyls (AAMAS, 2014)

Preface to the special issue: Adaptive Learning Agents, Part 1. - Sam Devlin; Daniel Hennes; Enda Howley (Connect. Sci., 2014)

Preface to the special issue: Adaptive Learning Agents Part 2. - Sam Devlin; Daniel Hennes; Enda Howley (Connect. Sci., 2014)

Survival of the Chartist: An Evolutionary Agent-Based Analysis of Stock Market Trading. - Daan Bloembergen; Daniel Hennes; Simon Parsons; Karl Tuyls (AAMAS, 2015)

Social Insect-Inspired Multi-Robot Coverage. - Bastian Broecker; Ipek Caliskanelli; Karl Tuyls; Elizabeth Sklar; Daniel Hennes (AAMAS, 2015)

Effective Approximations for Multi-Robot Coordination in Spatially Distributed Tasks. - Daniel Claes; Philipp Robbel; Frans A. Oliehoek; Karl Tuyls; Daniel Hennes; Wiebe van der Hoek (AAMAS, 2015)

Evolving Solutions to TSP Variants for Active Space Debris Removal. - Dario Izzo; Ingmar Getzner; Daniel Hennes; Luís Felismino Simões (GECCO, 2015)

Novelty Search for Soft Robotic Space Exploration. - Georgios Methenitis; Daniel Hennes; Dario Izzo; Arnoud Visser (GECCO, 2015)

Injection, Saturation and Feedback in Meta-Heuristic Interactions. - Krzysztof Nowak; Dario Izzo; Daniel Hennes (GECCO, 2015)

Interplanetary Trajectory Planning with Monte Carlo Tree Search. - Daniel Hennes; Dario Izzo (IJCAI, 2015)

Hybrid Insect-Inspired Multi-Robot Coverage in Complex Environments. - Bastian Broecker; Ipek Caliskanelli; Karl Tuyls; Elizabeth I. Sklar; Daniel Hennes (TAROS, 2015)

Trading in markets with noisy information: an evolutionary analysis. - Daan Bloembergen; Daniel Hennes; Peter McBurney; Karl Tuyls (Connect. Sci., 2015)

Preface to the special issue: Adaptive Learning Agents Part 3. - Sam Devlin; Daniel Hennes; Samuel Barrett (Connect. Sci., 2015)

Evolutionary Dynamics of Multi-Agent Learning: A Survey. - Daan Bloembergen; Karl Tuyls; Daniel Hennes; Michael Kaisers (J. Artif. Intell. Res., 2015)

Metastrategies in Large-Scale Bargaining Settings. - Daniel Hennes; Steven de Jong; Karl Tuyls; Ya'akov (Kobi) Gal (ACM Trans. Intell. Syst. Technol., 2015)

Fast approximators for optimal low-thrust hops between main belt asteroids. - Daniel Hennes; Dario Izzo; Damon Landau (SSCI, 2016)

Learning the optimal state-feedback using deep networks. - Carlos Sánchez-Sánchez; Dario Izzo; Daniel Hennes (SSCI, 2016)

Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance. - Kevin van Hecke; Guido de Croon; Laurens van der Maaten; Daniel Hennes; Dario Izzo (CoRR, 2016)

GTOC8: Results and Methods of ESA Advanced Concepts Team and JAXA-ISAS. - Dario Izzo; Daniel Hennes; Marcus Märtens; Ingmar Getzner; Krzysztof Nowak; Anna Heffernan; Stefano Campagnola; Chit Hong Yam; Naoya Ozaki; Yoshihide Sugimoto (CoRR, 2016)

Space Debris Removal: A Game Theoretic Analysis. - Richard Klíma; Daan Bloembergen; Rahul Savani; Karl Tuyls; Daniel Hennes; Dario Izzo (Games, 2016)

Gaussian process estimation of odometry errors for localization and mapping. - Javier Hidalgo-Carrióo; Daniel Hennes; Jakob Schwendner; Frank Kirchner (ICRA, 2017)

NOctoSLAM: Fast octree surface normal mapping and registration. - Joscha-David Fossel; Karl Tuyls; Benjamin Schnieders; Daniel Claes; Daniel Hennes (IROS, 2017)

Learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties. - Danny Drieß; Heiko Zimmermann; Simon Wolfen; Dan Suissa; Daniel F. B. Haeufle; Daniel Hennes; Marc Toussaint; Syn Schmitt (ICRA, 2018)

Active Multi-Contact Continuous Tactile Exploration with Gaussian Process Differential Entropy. - Danny Drieß; Daniel Hennes; Marc Toussaint (ICRA, 2019)

Neural Replicator Dynamics. - Shayegan Omidshafiei; Daniel Hennes; Dustin Morrill; Rémi Munos; Julien Pérolat; Marc Lanctot; Audrunas Gruslys; Jean-Baptiste Lespiau; Karl Tuyls (CoRR, 2019)

OpenSpiel: A Framework for Reinforcement Learning in Games. - Marc Lanctot; Edward Lockhart; Jean-Baptiste Lespiau; Vinícius Flores Zambaldi; Satyaki Upadhyay; Julien Pérolat; Sriram Srinivasan 005; Finbarr Timbers; Karl Tuyls; Shayegan Omidshafiei; Daniel Hennes; Dustin Morrill; et al. (CoRR, 2019)

Planning Approximate Exploration Trajectories for Model-Free Reinforcement Learning in Contact-Rich Manipulation. - Sabrina Hoppe; Zhongyu Lou; Daniel Hennes; Marc Toussaint (IEEE Robotics Autom. Lett., 2019)

Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. - Daniel Hennes; Dustin Morrill; Shayegan Omidshafiei; Rémi Munos; Julien Pérolat; Marc Lanctot; Audrunas Gruslys; Jean-Baptiste Lespiau; Paavo Parmas; Edgar A. Duéñez-Guzmán; Karl Tuyls (AAMAS, 2020)

A Generalized Training Approach for Multiagent Learning. - Paul Muller; Shayegan Omidshafiei; Mark Rowland 001; Karl Tuyls; Julien Pérolat; Siqi Liu 002; Daniel Hennes; Luke Marris; Marc Lanctot; Edward Hughes 001; Zhe Wang 055; Guy Lever; et al. (ICLR, 2020)

Fast computation of Nash Equilibria in Imperfect Information Games. - Rémi Munos; Julien Pérolat; Jean-Baptiste Lespiau; Mark Rowland 001; Bart De Vylder; Marc Lanctot; Finbarr Timbers; Daniel Hennes; Shayegan Omidshafiei; Audrunas Gruslys; Mohammad Gheshlaghi Azar; Edward Lockhart; et al. (ICML, 2020)

Navigating the Landscape of Games. - Shayegan Omidshafiei; Karl Tuyls; Wojciech M. Czarnecki; Francisco C. Santos; Mark Rowland 001; Jerome T. Connor; Daniel Hennes; Paul Muller; Julien Pérolat; Bart De Vylder; Audrunas Gruslys; Rémi Munos (CoRR, 2020)

Which priors matter? Benchmarking models for learning latent dynamics. - Aleksandar Botev; Andrew Jaegle; Peter Wirnsberger; Daniel Hennes; Irina Higgins (NeurIPS Datasets and Benchmarks, 2021)

Time-series Imputation of Temporally-occluded Multiagent Trajectories. - Shayegan Omidshafiei; Daniel Hennes; Marta Garnelo; Eugene Tarassov; Zhe Wang 055; Romuald Elie; Jerome T. Connor; Paul Muller; Ian Graham; William Spearman; Karl Tuyls (CoRR, 2021)

Evolutionary Dynamics and Φ-Regret Minimization in Games. - Georgios Piliouras; Mark Rowland 001; Shayegan Omidshafiei; Romuald Elie; Daniel Hennes; Jerome T. Connor; Karl Tuyls (CoRR, 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

NeuPL: Neural Population Learning. - Siqi Liu 002; Luke Marris; Daniel Hennes; Josh Merel; Nicolas Heess; Thore Graepel (ICLR, 2022)

Developing, evaluating and scaling learning agents in multi-agent environments. - Ian Gemp; Thomas W. Anthony 001; Yoram Bachrach; Avishkar Bhoopchand; Kalesha Bullard; Jerome T. Connor; Vibhavari Dasagi; Bart De Vylder; Edgar A. Duéñez-Guzmán; Romuald Elie; Richard Everett 001; Daniel Hennes; et al. (AI Commun., 2022)

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas W. Anthony 001; Stephen McAleer; Romuald Elie; et al. (CoRR, 2022)

Evolutionary Dynamics and Phi-Regret Minimization in Games. - Georgios Piliouras; Mark Rowland 001; Shayegan Omidshafiei; Romuald Elie; Daniel Hennes; Jerome T. Connor; Karl Tuyls (J. Artif. Intell. Res., 2022)

From motor control to team play in simulated humanoid football. - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech M. Czarnecki; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Sci. Robotics, 2022)

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech Czarnecki 001; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Zenodo, 2022)

Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas Anthony 001; Stephen McAleer; Romuald Elie; et al. (Zenodo, 2022)

Search-Improved Game-Theoretic Multiagent Reinforcement Learning in General and Negotiation Games. - Zun Li 002; Marc Lanctot; Kevin R. McKee; Luke Marris; Ian Gemp; Daniel Hennes; Kate Larson; Yoram Bachrach; Michael P. Wellman; Paul Muller (AAMAS, 2023)

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning. - Zun Li 002; Marc Lanctot; Kevin R. McKee; Luke Marris; Ian Gemp; Daniel Hennes; Paul Muller; Kate Larson; Yoram Bachrach; Michael P. Wellman (CoRR, 2023)

TacticAI: an AI assistant for football tactics. - Zhe Wang 055; Petar Velickovic; Daniel Hennes; Nenad Tomasev; Laurel Prince; Michael Kaisers; Yoram Bachrach; Romuald Elie; Li Kevin Wenliang; Federico Piccinini; William Spearman; Ian Graham; et al. (CoRR, 2023)

Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. - Marc Lanctot; John Schultz; Neil Burch; Max Olan Smith; Daniel Hennes; Thomas Anthony 001; Julien Pérolat (Trans. Mach. Learn. Res., 2023)

Approximating the Core via Iterative Coalition Sampling. - Ian Gemp; Marc Lanctot; Luke Marris; Yiran Mao; Edgar A. Duéñez-Guzmán; Sarah Perrin; András György 001; Romuald Elie; Georgios Piliouras; Michael Kaisers; Daniel Hennes; Kalesha Bullard; et al. (AAMAS, 2024)

Mastering Board Games by External and Internal Planning with Language Models. - John Schultz; Jakub Adámek; Matej Jusup; Marc Lanctot; Michael Kaisers; Sarah Perrin; Daniel Hennes; Jeremy Shar; Cannada Lewis; Anian Ruoss; Tom Zahavy; Petar Velickovic; et al. (CoRR, 2024)