[24] Lee, S., Seo, Y., Lee, K., Abbeel, P., and Shin, J. Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble. In Conference on Robot Learning, pp. 1702–1712. PMLR, 2022.
[25] Levine, S., Kumar, A., Tucker, G., and Fu, J. Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643, 2020.
[26] Liu, Y., Swaminathan, A., Agarwal, A., and Brunskill, E. Provably good batch reinforcement learning without great exploration. arXiv preprint arXiv:2007.08202, 2020.
[27] Lowe, R., Wu, Y. I., Tamar, A., Harb, J., Abbeel, O. P., and Mordatch, I. Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in neural information processing systems (NeurIPS), 2017.
[28] Meng, L., Wen, M., Yang, Y., Le, C., Li, X., Zhang, W., Wen, Y., Zhang, H., Wang, J., and Xu, B. Offline pre-trained multi-agent decision transformer: One big sequence model conquers all starcraftii tasks. arXiv preprint arXiv:2112.02845, 2021.
[29] Michie, D., Bain, M., and Hayes-Miches, J. Cognitive models from subcognitive skills. IEE control engineering series, 44:71-99, 1990.
[30] Nair, A., Dalal, M., Gupta, A., and Levine, S. Accelerating online reinforcement learning with offline datasets. arXiv preprint arXiv:2006.09359, 2020.
[31] Nair, A., Gupta, A., Dalal, M., and Levine, S. Awac: Accelerating online reinforcement learning with offline datasets. arXiv preprint arXiv:2006.09359, 2020.
[32] Oliehoek, F. A., Amato, C., et al. A concise introduction to decentralized POMDPs. Springer, 2016.
[33] Pan, L., Rashid, T., Peng, B., Huang, L., and Whiteson, S. Regularized softmax deep multi-agent q-learning. Advances in Neural Information Processing Systems, 34:1365-1377, 2021.
[34] Pan, L., Huang, L., Ma, T., and Xu, H. Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification. In International Conference on Machine Learning, pp. 17221-17237. PMLR, 2022.
[35] Peng, B., Rashid, T., Schroeder de Witt, C., Kamienny, P.-A., Torr, P., Böhmer, W., and Whiteson, S. Facmac: Factored multi-agent centralised policy gradients. Advances in Neural Information Processing Systems, 34:12208-12221, 2021.
[36] Peng, X. B., Kumar, A., Zhang, G., and Levine, S. Advantage-weighted regression: Simple and scalable off-policy reinforcement learning. arXiv preprint arXiv:1910.00177, 2019.
[37] Rashid, T., Samvelyan, M., De Witt, C. S., Farquhar, G., Foerster, J., and Whiteson, S. Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning. arXiv preprint arXiv:1803.11485, 2018.
[38] Rashid, T., Farquhar, G., Peng, B., and Whiteson, S. Weighted qmix: Expanding monotonic value function factorisation. arXiv e-prints, pp. arXiv-2006, 2020.
[39] Rashidinejad, P., Zhu, B., Ma, C., Jiao, J., and Russell, S. Bridging offline reinforcement learning and imitation learning: A tale of pessimism. Advances in Neural Information Processing Systems, 34, 2021.
[40] Samvelyan, M., Rashid, T., de Witt, C. S., Farquhar, G., Nardelli, N., Rudner, T. G., Hung, C.-M., Torr, P. H., Foerster, J., and Whiteson, S. The starcraft multi-agent challenge. arXiv preprint arXiv:1902.04043, 2019.
[41] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
[42] Son, K., Kim, D., Kang, W. J., Hostallero, D. E., and Yi, Y. Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning. In International Conference on Machine Learning (ICML), 2019.