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![1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Solar: Deep structured latent representations for
model-based reinf...](https://image.slidesharecdn.com/20190816-190816001737/75/DL-SOLAR-Deep-Structured-Representations-for-Model-Based-Reinforcement-Learning-1-2048.jpg?cb=1668053646)
![[余談] DSSMとそのRLにおける用途
DSSMのRLにおける用途 DSSM自体の工夫
Solar 最適制御（潜在変数の遷移が線形
となるようなDSSMを採用すること
により，画像観測においても
Linnear-Quadratic Regul...](https://image.slidesharecdn.com/20190816-190816001737/75/DL-SOLAR-Deep-Structured-Representations-for-Model-Based-Reinforcement-Learning-7-2048.jpg?cb=1668053646)
![関連研究
• DSSMの遷移モデルの設計が大事
• 特に線形だと制御理論の知見を活かせる，遷移行列のスペクトルが意味を持
つ[Johnson+2016]など，メリットがある
• 他に，線形の遷移モデルを組んでいる研究はあるだろうか？どんな生成/...](https://image.slidesharecdn.com/20190816-190816001737/75/DL-SOLAR-Deep-Structured-Representations-for-Model-Based-Reinforcement-Learning-15-2048.jpg?cb=1668053646)
![関連研究 E2C
• 「潜在空間上で線形の遷移モデルを学習し，それをLQRに利用する」というコン
セプトの初出
• 定式化がDSSMになっていない => 潜在変数がマルコフ性を満たす保証がない
[PlaNetで指摘]
• 遷移行列のパラメータ𝐴...](https://image.slidesharecdn.com/20190816-190816001737/75/DL-SOLAR-Deep-Structured-Representations-for-Model-Based-Reinforcement-Learning-16-2048.jpg?cb=1668053646)
