There has been substantial growth in the field of Multi-Agent Reinforcemnt Learning (MARL) in recent years. MARL covers a wide variety of problem fields which are constantly in a state of flux.
This is a collection of review papers for MARL and evaluation methods for RL in general. We sort papers by publication date and survey subtopic. Any additions to this repo are welcome. Although our current focus is more on evaluation, this repository aims to include all relevant subtopics related to MARL.
- Multi‑agent deep reinforcement learning: a survey by Sven Gronauer and Klaus Diepold. Artifcial Intelligence Review 2021.
- Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches by Annie Wong, Thomas Bäck, Anna V. Kononova and Aske Plaat. arXiv 2021.
- Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications by Thanh Thi Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi. arXiv 2019.
- Multiagent Learning: Basics, Challenges, and Prospects by Karl Tuyls, Gerhard Weiss. arXiv 2019.
- Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning by Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter Stone. arXiv 2011.
- How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments by Cedric Colas, Olivier Sigaud and Pierre-Yves Oudeyer.arXiv 2018.
- Deep Reinforcement Learning that Matters by Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup and David Meger. AAAI 2018.
- A Hitchhiker’s Guide to Statistical Comparisons of Reinforcement Learning Algorithms by Cédric Colas, Olivier Sigaud and Pierre-Yves Oudeyer. arXiv 2019.
- Evaluating the Performance of Reinforcement Learning Algorithms by Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas. ICML 2020.
- Deep Reinforcement Learning at the Edge of the Statistical Precipice by Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville and Marc G. Bellemare. NeurIPS 2021.
- Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks by Georgios Papoudakis, Filippos Christianos, Lukas Schäfer and Stefano V. Albrecht. NeurIPS 2021.
- Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot by Joel Z. Leibo, Edgar Du ́e ̃nez-Guzm ́an, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster ,Jayd Matyas, Charles Beattie, Igor Mordatch and Thore Graepel. PMLR 2021.
- IMPLEMENTATION MATTERS IN DEEP POLICY GRADIENTS: A CASE STUDY ON PPO AND TRPO by Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry. ICLR 2020.
- The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games by Chao Yu,Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen. arXiv 2021.
- RETHINKING THE IMPLEMENTATION TRICKS AND MONOTONICITY CONSTRAINT IN COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING by Jian Hu, Siyang Jiang, Seth Austin Harding, Haibin Wu and Shih-wei Liao. arXiv 2022.
- On Testing Machine Learning Programs by Houssem Ben Braieka, Foutse Khomh. Journal of Systems and Software 2018.
- Challenges of Real-World Reinforcement Learning by Gabriel Dulac-Arnold, Daniel Mankowitz and Todd Hester. ICML 2019.
- MEASURING THE RELIABILITY OF REINFORCEMENT LEARNING ALGORITHMS by Stephanie C.Y. Chan, Samuel Fishman, John Canny, Anoop Korattikara and Sergio Guadarrama. ICLR 2020.
- Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification by Davide Corsi, Enrico Marchesini and Alessandro Farinelli. arXiv 2020.
- Evaluating the Robustness of Collaborative Agents by Paul Knott, Micah Carroll, Sam Devlin, Kamil Ciosek, Katja Hofmann and A. D. Dragan. arXiv 2021.
- An empirical investigation of the challenges of real-world reinforcement learning by Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal and Todd Heste. arXiv 2021.
- Mava: a research framework for distributed multi-agent reinforcement learning by Arnu Pretorius, Kale-ab Tessera, Andries P. Smit, Kevin Eloff, Claude Formanek, St John Grimbly, Siphelele Danisa, Lawrence Francis, Jonathan Shock, Herman Kamper, Willie Brink, Herman Engelbrecht, Alexandre Laterre and Karim Beguir. arXiv 2021.
- Behaviour Suite for Reinforcement Learning by Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver and Hado Van Hasselt. arXiv 2020.
- The StarCraft Multi-Agent Challenge by Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, Shimon Whiteson. arXiv 2019.