Fetching the paper…
Reading the bibliography…
In multi-agent reinforcement learning, a commonly considered paradigm is centralized training with decentralized execution.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Geman, S.; and Geman, D. 1984 · 1984
Earlier work this paper cites.
Explaining the Gibbs sampler
Casella, G.; and George, E. I. 1992 · 1992
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Littman, M. L. 1994 · 1994
Earlier work this paper cites.
Coordinated reinforcement learning
Guestrin, C.; Lagoudakis, M.; and Parr, R. 2002 · 2002
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Bishop, C. 2006 · 2006
Earlier work this paper cites.
Deep implicit coordination graphs for multi-agent reinforcement learning
Li, S.; Gupta, J. K.; Morales, P.; Allen, R.; and Kochenderfer, M. J. 2020 · 2006
Earlier work this paper cites.
Distributed inference for latent dirichlet allocation
Newman, D.; Smyth, P.; Welling, M.; and Asuncion, A. 2007 · 2007
Earlier work this paper cites.
Conditional random fields for multi-agent reinforcement learning
Zhang, X.; Aberdeen, D.; and Vishwanathan, S. V. N. 2007 · 2007
Earlier work this paper cites.
Parallel gibbs sampling: From colored fields to thin junction trees
Gonzalez, J.; Low, Y.; Gretton, A.; and Guestrin, C. 2011 · 2011
Earlier work this paper cites.
Learning to communicate with Deep multi-agent reinforcement learning
Foerster, J. N.; Assael, Y. M.; de Freitas, N.; and Whiteson, S. 2016 · 2016
Earlier work this paper cites.
Learning multiagent communication with backpropagation
Sukhbaatar, S.; Szlam, A.; and Fergus, R. 2016 · 2016
Earlier work this paper cites.
VAIN: Attentional Multi-agent Predictive Modeling
Hoshen, Y. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 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, P.; OpenAI; and Mordatch, I. 2017 · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
Cited alongside, same era.
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 · 2017
Cited alongside, same era.
Efficient MCMC for Gibbs random fields using pre-computation
Boland, A.; Friel, N.; and Maire, F. 2018 · 2018
Cited alongside, same era.
Counterfactual multi-agent policy gradients
Foerster, J. N.; Farquhar, G.; Afouras, T.; Nardelli, N.; and Whiteson, S. 2018 · 2018
Cited alongside, same era.
Graph convolutional reinforcement learning
Jiang, J.; Dun, C.; Huang, T.; and Lu, Z. 2018 · 2018
Networked Multi-Agent Reinforcement Learning with Emergent Communication
Gupta, S.; Hazra, R.; and Dukkipati, A. 2020 · 2020
Later among the works it cites.
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. 2020 · 2020
Later among the works it cites.
Multi-agent actor-critic with hierarchical graph attention network
Ryu, H.; Shin, H.; and Park, J. 2020 · 2020
Later among the works it cites.
Reinforcement Learning Benchmarks for Traffic Signal Control
Ault, J.; and Sharon, G. 2021 · 2021
Later among the works it cites.
Powernet: Multi-agent deep reinforcement learning for scalable powergrid control
Chen, D.; Chen, K.; Li, Z.; Chu, T.; Yao, R.; Qiu, F.; and Lin, K. 2021 · 2021
Later among the works it cites.
Settling the variance of multi-agent policy gradients
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning attentional communication for multi-agent cooperation
Jiang, J.; and Lu, Z. 2018 · 2018
Cited alongside, same era.
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Zhang, K.; Yang, Z.; Liu, H.; Zhang, T.; and Basar, T. 2018 · 2018
Cited alongside, same era.
Tarmac: Targeted multi-agent communication
Das, A.; Gervet, T.; Romoff, J.; Batra, D.; Parikh, D.; Rabbat, M.; and Pineau, J. 2019 · 2019
Cited alongside, same era.
Learning to schedule communication in multi-agent reinforcement learning
Kim, D.; Moon, S.; Hostallero, D.; Kang, W. J.; Lee, T.; Son, K.; and Yi, Y. 2019 · 2019
Cited alongside, same era.
Individualized Controlled Continuous Communication Model for Multiagent Cooperative and Competitive Tasks
Singh, A.; Jain, T.; and Sukhbaatar, S. 2019 · 2019
Cited alongside, same era.
Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Son, K.; Kim, D.; Kang, W. J.; Hostallero, D. E.; and Yi, Y. 2019 · 2019
Cited alongside, same era.
Deep Coordination Graphs
Boehmer, W.; Kurin, V.; and Whiteson, S. 2020 · 2020
Cited alongside, same era.
Kuba, J. G.; Wen, M.; Meng, L.; Zhang, H.; Mguni, D.; Wang, J.; Yang, Y.; et al. 2021 · 2021
Later among the works it cites.
Multi-Agent Graph-Attention Communication and Teaming
Niu, Y.; Paleja, R.; and Gombolay, M. 2021 · 2021
Later among the works it cites.
Decentralized multi-agent reinforcement learning with networked agents: Recent advances
Zhang, K.; Yang, Z.; and Başar, T. 2021 · 2021
Later among the works it cites.
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
Kuba, J. G.; Chen, R.; Wen, M.; Wen, Y.; Sun, F.; Wang, J.; and Yang, Y. 2022 · 2022
Later among the works it cites.
Scalable reinforcement learning for multiagent networked systems
Qu, G.; Wierman, A.; and Li, N. 2022 · 2022
Later among the works it cites.
Multi-agent reinforcement learning is a sequence modeling problem
Wen, M.; Kuba, J.; Lin, R.; Zhang, W.; Wen, Y.; Wang, J.; and Yang, Y. 2022 · 2022
Later among the works it cites.
The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2022 · 2022
Later among the works it cites.
GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning
Hao, Q.; Huang, W.; Feng, T.; Yuan, J.; and Li, Y. 2023 · 2023
Later among the works it cites.
Efficient and scalable reinforcement learning for large-scale network control
Ma, C.; Li, A.; Du, Y.; Dong, H.; and Yang, Y. 2024 · 2024
Closest in time.