【DL輪読会】Is Conditional Generative Modeling All You Need For Decision-Making?
1. DEEP LEARNING JP
[DL Papers]
“Is Conditional Generative Modeling All You
Need For Decision-Making?”
Presenter: Manato Yaguchi B4
(Hokkaido University)
http://deeplearning.jp/
3. 1. 書誌情報
紹介論文
タイトル: Is Conditional Generative Modeling All You Need For Decision-Making?(arxiv)
ICLR2023(top 5%)
出典: ArXiv (2022. 11)
著者: Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal
Improbable AI Lab Operations Research Center Computer Science and Artificial Intelligence Lab Massachusetts Institute of
Technology
プロジェクトページ
概要
- 条件付き拡散モデルにより、強化学習に代わる手法として意思決定問題を行った
- 報酬で条件づけられた拡散モデルとして方策をモデル化することで、強化学習に見られる
複雑さを排除
- 制約やスキル等の他の条件変数も適用でき、かつ複数の制約を同時に組み合わせることが
可能 3
17. 引用
17
[1] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen.
Hierarchical textconditional image generation with clip latents. arXiv preprint
arXiv:2204.06125, 2022.
[2] Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk
Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo
Gutman-Solo, et al. Solving quantitative reasoning problems with language models.
arXiv preprint arXiv:2206.14858, 2022.
[3] Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4RL:
Datasets for deep data-driven reinforcement learning. arXiv preprint
arXiv:2004.07219, 2020.
[4] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic
models. In Advances in Neural Information Processing Systems, 2020.
[5] [DL輪読会]GLIDE: Guided Language to Image Diffusion for Generation and …
(slideshare.net)