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)