[59] Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. *Dueling network architectures for deep reinforcement learning*. In *International conference on machine learning*, pages 1995–2003. PMLR, 2016.
[60] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[61] Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Reinforcement learning, pages 5-32, 1992.
[62] Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour. Policy gradient methods for reinforcement learning with function approximation. Advances in neural information processing systems, 12, 1999.
[63] Vijay Konda and John Tsitsiklis. Actor-critic algorithms. Advances in neural information processing systems, 12, 1999.
[64] Lex Weaver and Nigel Tao. The optimal reward baseline for gradient-based reinforcement learning. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, page 538-545, 2001.
[65] Yu-Han Chang, Tracey Ho, and Leslie Kaelbling. All learning is local: Multi-agent learning in global reward games. Advances in neural information processing systems, 16, 2003.
[66] David H. Wolpert and Kagan Tumer. Optimal payoff functions for members of collectives. Advances in Complex Systems, 4(2/3):265-279, 2001.
[67] Tabish Rashid, Gregory Farquhar, Bei Peng, and Shimon Whiteson. Weighted QMIX: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 33:10199-10210, 2020.
[68] Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson. MAVEN: Multiagent variational exploration. Advances in Neural Information Processing Systems, 32, 2019.
[69] Raphaël Avalos, Mathieu Reymond, Ann Nowé, and Diederik M. Roijers. Local advantage networks for cooperative multi-agent reinforcement learning. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS '22, page 1524-1526, 2022.
[70] Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, and Dacheng Tao. LIIR: Learning individual intrinsic reward in multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 32, 2019.
[71] Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen, Ying Wen, Fanglei Sun, Jun Wang, and Yaodong Yang. Trust region policy optimisation in multi-agent reinforcement learning. International Conference on Learning Representations, 2021.
[72] John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In International conference on machine learning, pages 1889-1897. PMLR, 2015.
[73] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. CoRR, abs/1707.06347, 2017.
[74] Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, and Yaodong Yang. Multi-agent reinforcement learning is a sequence modeling problem. In Advances in Neural Information Processing Systems, 2022.
[75] Andy B. Yoo, Morris A. Jette, and Mark Grondona. Slurm: Simple linux utility for resource management. In Job Scheduling Strategies for Parallel Processing: 9th International Workshop, pages 44-60. Springer, 2003.