J. Tang, F. Wolski, and S. Zhang, “Dota 2 with large scale deep reinforcement learning.” arXiv preprint arXiv:1912.06680, 2019.
[10] P. Sunehag, G. Lever, A. Gruslys, W. M. Czarni, V. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls et al., “Value-decomposition networks for cooperative multiagent learning based on team reward,” in International Conference on Autonomous Agents and MultiAgent Systems, 2018, pp. 2085–2087.
[11] T. Rashid, M. Samvelyan, C. Schroeder, G. Farquhar, J. Foerster, and S. Whiteson, “Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning,” in International Conference on Machine Learning, 2018, pp. 4295–4304.
[12] K. Son, D. Kim, W. J. Kang, D. E. Hostallero, and Y. Yi, “Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning,” in International Conference on Machine Learning, 2019, pp. 5887–5896.
[13] Y. Yang, J. Hao, B. Liao, K. Shao, G. Chen, W. Liu, and H. Tang, "Qatten: A general framework for cooperative multiagent reinforcement learning," arXiv preprint arXiv:2002.03939, 2020.
[14] J. Wang, Z. Ren, T. Liu, Y. Yu, and C. Zhang, “Qplex: Duplex dueling multi-agent q-learning,” in International Conference on Learning Representations, 2021.
[15] R. Lowe, Y. Wu, A. Tamar, J. Harb, O. P. Abbeel, and I. Mordatch, "Multi-agent actor-critic for mixed cooperative-competitive environments," in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 6379–6390.
[16] J. Foerster, G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson, "Counterfactual multi-agent policy gradients," in AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
[17] Y. Wang, B. Han, T. Wang, H. Dong, and C. Zhang, “Dop: Off-policy multi-agent decomposed policy gradients,” in International Conference on Learning Representations, 2021.
[18] J. G. Kuba, R. Chen, M. Wen, Y. Wen, F. Sun, J. Wang, and Y. Yang, "Trust region policy optimisation in multi-agent reinforcement learning," in International Conference on Learning Representations, 2022.
[19] T. M. Moldovan and P. Abbeel, “Safe exploration in markov decision processes,” in International Conference on Machine Learning, 2012, pp. 1451–1458.
[20] J. Achiam, D. Held, A. Tamar, and P. Abbeel, “Constrained policy optimization,” in International Conference on Machine Learning, 2017, pp. 22-31.
[21] A. Ray, J. Achiam, and D. Amodei, "Benchmarking safe exploration in deep reinforcement learning," arXiv preprint arXiv:1910.01708, vol. 7, no. 1, p. 2, 2019.
[22] Y. Zhang, Q. Vuong, and K. Ross, "First order constrained optimization in policy space," Advances in Neural Information Processing Systems, vol. 33, pp. 15338-15349, 2020.
[23] T. Xu, Y. Liang, and G. Lan, "Crpo: A new approach for safe reinforcement learning with convergence guarantee," in International Conference on Machine Learning, 2021, pp. 11480-11491.
[24] S. Gu, J. G. Kuba, Y. Chen, Y. Du, L. Yang, A. Knoll, and Y. Yang, "Safe multi-agent reinforcement learning for multi-robot control," Artificial Intelligence, p. 103905, 2023.
[25] D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané, “Concrete problems in ai safety,” arXiv preprint arXiv:1606.06565, 2016.
[26] S. Shalev-Shwartz, S. Shammah, and A. Shashua, "Safe, multi-agent, reinforcement learning for autonomous driving," arXiv preprint arXiv:1610.03295, 2016.
[27] C. Liu, N. Geng, V. Aggarwal, T. Lan, Y. Yang, and M. Xu, "Cmix: Deep multi-agent reinforcement learning with peak and average constraints," in Machine Learning and Knowledge Discovery in Databases., 2021, pp. 157-173.