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Learning a world model for model-free Reinforcement Learning (RL) agents can significantly improve the sample efficiency by learning policies in imagination.
Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Adam: A method for stochastic optimization
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Gaussian error linear units (gelus)
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A concise introduction to decentralized POMDPs , volume 1
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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Attention is all you need
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Counterfactual multi-agent policy gradients
Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, and Thore Graepel · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, and Shimon Whiteson · 2019
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QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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A very condensed survey and critique of multiagent deep reinforcement learning
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor · 2020
FACMAC: Factored multi-agent centralised policy gradients
Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Boehmer, and Shimon Whiteson · 2021
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QPLEX: Duplex dueling multi-agent q-learning
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang · 2021
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Mambpo: Sample-efficient multi-robot reinforcement learning using learned world models
Daniël Willemsen, Mario Coppola, and Guido CHE de Croon · 2021
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2022
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Transdreamer: Reinforcement learning with transformer world models
Chang Chen, Yi-Fu Wu, Jaesik Yoon, and Sungjin Ahn · 2022
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mingpt: A minimal pytorch re-implementation of the openai gpt (generative pretrained transformer) training
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Multi-agent game abstraction via graph attention neural network
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Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
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Multi-agent actor-critic with hierarchical graph attention network
Heechang Ryu, Hayong Shin, and Jinkyoo Park · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
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Model based reinforcement learning for atari
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Deep reinforcement learning at the edge of the statistical precipice
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Temporal difference learning for model predictive control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2022
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Mingling foresight with imagination: Model-based cooperative multi-agent reinforcement learning
Zhiwei Xu, Dapeng Li, Bin Zhang, Yuan Zhan, Yunpeng Baiia, and Guoliang Fan · 2022
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The surprising effectiveness of PPO in cooperative multi-agent games
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SMACv2: An improved benchmark for cooperative multi-agent reinforcement learning
Benjamin Ellis, Jonathan Cook, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Mahajan, Jakob Nicolaus Foerster, and Shimon Whiteson · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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Transformers are sample-efficient world models
Vincent Micheli, Eloi Alonso, and François Fleuret · 2023
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Transformer-based world models are happy with 100k interactions
Jan Robine, Marc Höftmann, Tobias Uelwer, and Stefan Harmeling · 2023
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TD-MPC2: Scalable, robust world models for continuous control
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Efficient multi-agent reinforcement learning by planning
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Codreamer: Communication-based decentralised world models
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