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Decision Transformer (DT), a trajectory modelling method, has shown competitive performance compared to traditional offline reinforcement learning (RL) approaches on various classic control tasks.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 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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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2021
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
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Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B Tenenbaum, and Sergey Levine · 2022
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Mildly conservative q-learning for offline reinforcement learning
Jiafei Lyu, Xiaoteng Ma, Xiu Li, and Zongqing Lu · 2022
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Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2022
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Prompting decision transformer for few-shot policy generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua Tenenbaum, and Chuang Gan · 2022
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Metaems: A meta reinforcement learning-based control framework for building energy management system
Huiliang Zhang, Di Wu, and Benoit Boulet · 2022
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Online decision transformer
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Is conditional generative modeling all you need for decision-making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal · 2023
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When should we prefer decision transformers for offline reinforcement learning?
Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, and Amy Zhang · 2023
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Hierarchical decision transformer
André Correia and Luís A Alexandre · 2023
Cited alongside, same era.
A survey on generative diffusion models
Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li · 2024
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Soham De, Samuel L Smith, Anushan Fernando, Aleksandar Botev, George Cristian-Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, et al · 2024
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Cleandiffuser: An easy-to-use modularized library for diffusion models in decision making
Zibin Dong, Yifu Yuan, Jianye Hao, Fei Ni, Yi Ma, Pengyi Li, and Yan Zheng · 2024
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Linjiajie Fang, Ruoxue Liu, Jing Zhang, Wenjia Wang, and Bing-Yi Jing · 2024
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Goal-conditioned data augmentation for offline reinforcement learning
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Sample efficient offline-to-online reinforcement learning
Siyuan Guo, Lixin Zou, Hechang Chen, Bohao Qu, Haotian Chi, Philip S Yu, and Yi Chang · 2023
Cited alongside, same era.
Traffic signal control using lightweight transformers: An offline-to-online rl approach
Xingshuai Huang, Di Wu, and Benoit Boulet · 2023
Cited alongside, same era.
A bayesian reinforcement learning method for periodic robotic control under significant uncertainty
Yuanyuan Jia, Pedro Miguel Uriguen Eljuri, and Tadahiro Taniguchi · 2023
Cited alongside, same era.
Efficient diffusion policies for offline reinforcement learning
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, and Shuicheng Yan · 2023
Cited alongside, same era.
Contrastive decision transformers
Sachin G Konan, Esmaeil Seraj, and Matthew Gombolay · 2023
Cited alongside, same era.
Adaptdiffuser: Diffusion models as adaptive self-evolving planners
Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, and Ping Luo · 2023
Cited alongside, same era.
Xingshuai Huang, Di Wu Member, and Benoit Boulet · 2024
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Diffstitch: Boosting offline reinforcement learning with diffusion-based trajectory stitching
Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, and Weinan Zhang · 2024
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Eagle and finch: Rwkv with matrix-valued states and dynamic recurrence
Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Teddy Ferdinan, Haowen Hou, Przemysław Kazienko, et al · 2024
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Learning to (learn at test time): Rnns with expressive hidden states
Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, et al · 2024
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Is mamba effective for time series forecasting?
Zihan Wang, Fanheng Kong, Shi Feng, Ming Wang, Xiaocui Yang, Han Zhao, Daling Wang, and Yifei Zhang · 2024
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Diffusion models without attention
Jing Nathan Yan, Jiatao Gu, and Alexander M Rush · 2024
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A survey on visual mamba
Hanwei Zhang, Ying Zhu, Dan Wang, Lijun Zhang, Tianxiang Chen, Ziyang Wang, and Zi Ye · 2024
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Deep reinforcement learning in real-time strategy games: a systematic literature review
Gabriel Caldas Barros e Sá and Charles Andrye Galvão Madeira · 2025
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A critical review of safe reinforcement learning strategies in power and energy systems
Van-Hai Bui, Sina Mohammadi, Srijita Das, Akhtar Hussain, Guilherme Vieira Hollweg, and Wencong Su · 2025
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Dime: Diffusion-based maximum entropy reinforcement learning
Onur Celik, Zechu Li, Denis Blessing, Ge Li, Daniel Palenicek, Jan Peters, Georgia Chalvatzaki, and Gerhard Neumann · 2025
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Fdpp: Fine-tune diffusion policy with human preference
Yuxin Chen, Devesh K Jha, Masayoshi Tomizuka, and Diego Romeres · 2025
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Maximum entropy reinforcement learning with diffusion policy
Xiaoyi Dong, Jian Cheng, and Xi Sheryl Zhang · 2025
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Sample-efficient preference-based reinforcement learning using diffusion models
Jingjing Feng, Lucheng Wang, Alona Tenytska, and Bei Peng · 2025
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Reward-safety balance in offline safe rl via diffusion regularization
Junyu Guo, Zhi Zheng, Donghao Ying, Ming Jin, Shangding Gu, Costas Spanos, and Javad Lavaei · 2025
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Efficient online reinforcement learning for diffusion policy
Haitong Ma, Tianyi Chen, Kai Wang, Na Li, and Bo Dai · 2025
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Hgat and multi-agent rl-based method for multi-intersection traffic signal control
Ziyang Zhai, Ruru Hao, Boyang Cui, and Siyi Wang · 2025
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