Fetching the paper…
Reading the bibliography…
Learning to collaborate is critical in Multi-Agent Reinforcement Learning (MARL).
Rode: Learning roles to decompose multi-agent tasks
Wang, T., Gupta, T., Mahajan, A., Peng, B., Whiteson, S., and Zhang, C · 2010
Earlier work this paper cites.
Coordinated multi-robot exploration under communication constraints using decentralized markov decision processes
Matignon, L., Jeanpierre, L., and Mouaddib, A · 2012
Earlier work this paper cites.
A concise introduction to decentralized POMDPs
Oliehoek, F. A. and Amato, C · 2016
Earlier work this paper cites.
A unified game-theoretic approach to multiagent reinforcement learning
Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Pérolat, J., Silver, D., and Graepel, T · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R., Wu, Y. I., Tamar, A., Harb, J., Abbeel, O. P., and Mordatch, I · 2017
Earlier work this paper cites.
Peng, P., Wen, Y., Yang, Y., Yuan, Q., Tang, Z., Long, H., and Wang, J · 2017
Earlier work this paper cites.
Value-decomposition networks for cooperative multi-agent learning
Sunehag, P., Lever, G., Gruslys, A., Czarnecki, W. M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J. Z., Tuyls, K., et al · 2017
Earlier work this paper cites.
Mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D · 2018
Earlier work this paper cites.
Generative adversarial self-imitation learning
Guo, Y., Oh, J., Singh, S., and Lee, H · 2018
Earlier work this paper cites.
Intrinsic social motivation via causal influence in multi-agent RL
Jaques, N., Lazaridou, A., Hughes, E., Gülçehre, Ç., Ortega, P. A., Strouse, D., Leibo, J. Z., and de Freitas, N · 2018
Earlier work this paper cites.
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Rashid, T., Samvelyan, M., De Witt, C. S., Farquhar, G., Foerster, J., and Whiteson, S · 2018
Earlier work this paper cites.
Hierarchical deep multiagent reinforcement learning with temporal abstraction
Tang, H., Hao, J., Lv, T., Chen, Y., Zhang, Z., Jia, H., Ren, C., Zheng, Y., Meng, Z., Fan, C., et al · 2018
Earlier work this paper cites.
Weighted double deep multiagent reinforcement learning in stochastic cooperative environments
Zheng, Y., Meng, Z., Hao, J., and Zhang, Z · 2018
Cited alongside, same era.
A deep bayesian policy reuse approach against non-stationary agents
Zheng, Y., Meng, Z., Hao, J., Zhang, Z., Yang, T., and Fan, C · 2018
Cited alongside, same era.
A survey and critique of multiagent deep reinforcement learning
Hernandez-Leal, P., Kartal, B., and Taylor, M. E · 2019
Cited alongside, same era.
Actor-attention-critic for multi-agent reinforcement learning
Iqbal, S. and Sha, F · 2019
Cited alongside, same era.
Social influence as intrinsic motivation for multi-agent deep reinforcement learning
Jaques, N., Lazaridou, A., Hughes, E., Gulcehre, C., Ortega, P., Strouse, D., Leibo, J. Z., and De Freitas, N · 2019
Cited alongside, same era.
Efficient ridesharing order dispatching with mean field multi-agent reinforcement learning
A maximum mutual information framework for multi-agent reinforcement learning
Kim, W., Jung, W., Cho, M., and Sung, Y · 2020
Later among the works it cites.
Multi-agent interactions modeling with correlated policies
Liu, M., Zhou, M., Zhang, W., Zhuang, Y., Wang, J., Liu, W., and Yu, Y · 2020
Later among the works it cites.
Generating behavior-diverse game ais with evolutionary multi-objective deep reinforcement learning
Shen, R., Zheng, Y., Hao, J., Meng, Z., Chen, Y., Fan, C., and Liu, Y · 2020
Later among the works it cites.
Continuous multiagent control using collective behavior entropy for large-scale home energy management
Sun, J., Zheng, Y., Hao, J., Meng, Z., and Liu, Y · 2020
Later among the works it cites.
Learning latent representations to influence multi-agent interaction
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Li, M., Qin, Z., Jiao, Y., Yang, Y., Wang, J., Wang, C., Wu, G., and Ye, J · 2019
Cited alongside, same era.
Maven: Multi-agent variational exploration
Mahajan, A., Rashid, T., Samvelyan, M., and Whiteson, S · 2019
Cited alongside, same era.
The starcraft multi-agent challenge
Samvelyan, M., Rashid, T., de Witt, C. S., Farquhar, G., Nardelli, N., Rudner, T. G. J., Hung, C., Torr, P. H. S., Foerster, J. N., and Whiteson, S · 2019
Cited alongside, same era.
Probabilistic recursive reasoning for multi-agent reinforcement learning
Wen, Y., Yang, Y., Luo, R., Wang, J., and Pan, W · 2019
Cited alongside, same era.
Efficient communication in multi-agent reinforcement learning via variance based control
Zhang, S. Q., Zhang, Q., and Lin, J · 2019
Cited alongside, same era.
Club: A contrastive log-ratio upper bound of mutual information
Cheng, P., Hao, W., Dai, S., Liu, J., Gan, Z., and Carin, L · 2020
Cited alongside, same era.
Deep multi-agent reinforcement learning for decentralized continuous cooperative control
de Witt, C. S., Peng, B., Kamienny, P.-A., Torr, P., Böhmer, W., and Whiteson, S · 2020
Cited alongside, same era.
Xie, A., Losey, D. P., Tolsma, R., Finn, C., and Sadigh, D · 2020
Later among the works it cites.
Kogun: Accelerating deep reinforcement learning via integrating human suboptimal knowledge
Zhang, P., Hao, J., Wang, W., Tang, H., Ma, Y., Duan, Y., and Zheng, Y · 2020
Later among the works it cites.
Efficient multiagent policy optimization based on weighted estimators in stochastic cooperative environments
Zheng, Y., Hao, J., Zhang, Z., Meng, Z., and Hao, X · 2020
Later among the works it cites.
Signal instructed coordination in cooperative multi-agent reinforcement learning
Chen, L., Guo, H., Du, Y., Fang, F., Zhang, H., Zhang, W., and Yu, Y · 2021
Later among the works it cites.
Rethinking the implementation tricks and monotonicity constraint in cooperative multi-agent reinforcement learning
Hu, J., Jiang, S., Harding, S. A., Wu, H., and Liao, S.-w · 2021
Later among the works it cites.
Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation
Li, B., Tang, H., Zheng, Y., Hao, J., Li, P., Wang, Z., Meng, Z., and Wang, L · 2021
Later among the works it cites.
Lief: Learning to influence through evaluative feedback
Merhej, R. and Chetouani, M · 2021
Later among the works it cites.
A deeper understanding of state-based critics in multi-agent reinforcement learning
Lyu, X., Baisero, A., Xiao, Y., and Amato, C · 2022
Closest in time.