Fetching the paper…
Reading the bibliography…
This paper investigates the model-based methods in multi-agent reinforcement learning (MARL).
Stochastic games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Richard S Sutton · 1990
Earlier work this paper cites.
Convergence of stochastic iterative dynamic programming algorithms
Tommi Jaakkola, Michael I Jordan, and Satinder P Singh · 1994
Earlier work this paper cites.
A unified analysis of value-function-based reinforcement-learning algorithms
Csaba Szepesvári and Michael L Littman · 1999
Earlier work this paper cites.
Nash q-learning for general-sum stochastic games
Junling Hu and Michael P Wellman · 2003
Earlier work this paper cites.
Weighted sup-norm contractions in dynamic programming: A review and some new applications
Dimitri P Bertsekas · 2012
Earlier work this paper cites.
Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
Earlier work this paper cites.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
Earlier work this paper cites.
Learning with opponent-learning awareness
Jakob Foerster, Richard Y Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, and Igor Mordatch · 2018
Cited alongside, same era.
Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
Cited alongside, same era.
Is q-learning provably efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan · 2018
Cited alongside, same era.
Data-efficient reinforcement learning with probabilistic model predictive control
Sanket Kamthe and Marc Deisenroth · 2018
Cited alongside, same era.
Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
Cited alongside, same era.
Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, and Tengyu Ma · 2019
Later among the works it cites.
Multi-agent reinforcement learning with approximate model learning for competitive games
Young Joon Park, Yoon Sang Cho, and Seoung Bum Kim · 2019
Later among the works it cites.
Value propagation for decentralized networked deep multi-agent reinforcement learning
Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, and Junwu Xiong · 2019
Later among the works it cites.
A regularized opponent model with maximum entropy objective
Zheng Tian, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou, and Jun Wang · 2019
Later among the works it cites.
Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ermo Wei, Drew Wicke, David Freelan, and Sean Luke · 2018
Cited alongside, same era.
Qmix: Monotonic value function factorisation for deep multi- agent reinforcement learning
Shimon Whiteson · 2018
Cited alongside, same era.
Mean field multi-agent reinforcement learning
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang · 2018
Cited alongside, same era.
Fully decentralized multi-agent reinforcement learning with networked agents
Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, et al · 2018
Cited alongside, same era.
Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
Cited alongside, same era.
When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Cited alongside, same era.
Multi agent reinforcement learning with multi-step generative models
Orr Krupnik, Igor Mordatch, and Aviv Tamar · 2019
Cited alongside, same era.
Probabilistic recursive reasoning for multi-agent reinforcement learning
Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, and W Pan · 2019
Later among the works it cites.
Model-augmented actor-critic: Backpropagating through paths
Ignasi Clavera, Yao Fu, and Pieter Abbeel · 2020
Later among the works it cites.
A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications
Wei Du and Shifei Ding · 2020
Later among the works it cites.
Multi-agent trajectory prediction with fuzzy query attention
Nitin Kamra, Hao Zhu, Dweep Trivedi, Ming Zhang, and Yan Liu · 2020
Later among the works it cites.
Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning
Jiachen Li, Fan Yang, Masayoshi Tomizuka, and Chiho Choi · 2020
Later among the works it cites.
Multi-agent actor-critic with hierarchical graph attention network
Heechang Ryu, Hayong Shin, and Jinkyoo Park · 2020
Later among the works it cites.
Learning to communicate implicitly by actions
Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou-Ammar, and Jun Wang · 2020
Later among the works it cites.