Accepted Papers
The accepted papers are listed below (PDFs are also available for papers whose authors gave upload permission):
Paul Van Eecke and Katrien Beuls. Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning (PDF)
Wolfram Barfuss. Towards a unified treatment of the dynamics of collective learning (PDF)
Kyle Tilbury and Jesse Hoey. Multi-Agent Reinforcement Learning and Human Social Factors in Climate Change Mitigation (PDF)
Xiaobai Ma, Jayesh K. Gupta and Mykel J. Kochenderfer. Policy Representation in Continuous Action Games (PDF)
Dong-Ki Kim, Miao Liu, Matthew Riemer, Golnaz Habibi, Sebastian Lopez-Cot, Samir Wadhwania, Gerald Tesauro and Jonathan How. A Policy Gradient Theorem for Learning to Learn in Multiagent Reinforcement Learning (PDF)
Mahak Goindani and Jennifer Neville. Social Reinforcement Learning (PDF)
Alexander Shmakov, John Lanier, Stephen McAleer, Rohan Achar, Christina Lopes and Pierre Baldi. ColosseumRL: A Framework for Multiagent Reinforcement Learning in N-Player Games
Luiz Antonio Celiberto Junior and Reinaldo A. C. Bianchi. Transfer Learning by Reputation in Large Multi-Agent System
Elad Liebman, Alexandre Ardel, Shashank Bassi, Jacob Riedel and Edgars Vitolins. Autonomous Multiagent Aviation: Challenges and Opportunities (PDF)
Stefan Heidekrüger, Nils Kohring, Paul Sutterer and Martin Bichler. Multiagent Learning for Equilibrium Computation in Auction Markets (PDF)
Clement Moulin-Frier and Pierre-Yves Oudeyer. Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges (PDF)
Theocharis Kravaris and George Vouros. Deep Multi-Agent Reinforcement Learning Methods Addressing the Scalability Challenge (PDF)
William Birmingham, Sarah Dumnich and Britton Wolfe. Separate worlds, separate clocks: issues in asynchronous MDPs (PDF)
Tushant Jha. The Role of Artificial Institutions in Multi Agent Learning: A Research Agenda
Qi Zhang. Meta-Learning Multi-Agent Communication
Chirag Chhablani and Ian Kash. Position Paper: Regret Minimization for Stateful, Cooperative Settings
Bengisu Güresti and Nazim Kemal Ure. Evaluating Generalization and Transfer Capacity of Multi-Agent Reinforcement Learning Across Variable Number of Agents (PDF)
William A. Dawson, Ruben Glatt, Edward Rusu, Braden C. Soper and Ryan A. Goldhahn. Hybrid Information-driven Multi-agent Reinforcement Learning (PDF)
Stephen Cranefield. Learning Norms in Multi-Agent Systems: A Challenge to the MARL Community (PDF)
Aleksander Czechowski. Constraint Propagation and Reverse Multi-Agent Learning (PDF)