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![DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Learning Task Informed Abstractions
発表者: 阿久澤圭 (松尾研D3)](https://image.slidesharecdn.com/20210709akuzawa-210709021836/75/DL-Learning-Task-Informed-Abstractions-1-2048.jpg?cb=1667364226)
![関連研究: Dreamer [Hafner et. al. 2020]
• (a) 画像・観測予測によって表現（緑色の点）を学習
• (b) 表現を入力に取る価値関数・方策を訓練（データはモデル自身から生成）
• 欠点（発表論文での指摘）: 表...](https://image.slidesharecdn.com/20210709akuzawa-210709021836/75/DL-Learning-Task-Informed-Abstractions-6-2048.jpg?cb=1667364226)
![関連研究: DeepMDP [Gelada et. al. 2019]
• 二つの損失関数で表現学習
• 報酬の予測
• 次の時刻の表現（の分布）の予測
• メリット:
• 画像予測を用いないため，表現は
distractor-free
• デ...](https://image.slidesharecdn.com/20210709akuzawa-210709021836/75/DL-Learning-Task-Informed-Abstractions-7-2048.jpg?cb=1667364226)
![関連研究: MISA [Zhang et. al. 2020]
• 提案:
• グラフィカルモデル上で状態を
タスク関連・非関連に分離する
ことを提案
• 欠点（紹介論文の指摘）:
• アイデア自体は紹介論文と同じ
• practicalな手法...](https://image.slidesharecdn.com/20210709akuzawa-210709021836/75/DL-Learning-Task-Informed-Abstractions-8-2048.jpg?cb=1667364226)
![関連研究: DBC [Zhang et. al. 2021]
• 提案:
• bisimulation metricsを用
いた，タスク関連・非関連
の特徴量の分離
• DeepMDPと似ている（画
像予測を学習に用いない）
• 欠点（発表論文...](https://image.slidesharecdn.com/20210709akuzawa-210709021836/75/DL-Learning-Task-Informed-Abstractions-9-2048.jpg?cb=1667364226)
