4. 貢献1:現実的なデータセット
4
[Mirowski et al., 2016]
[Mirowski etal., 2016] “Learning to Navigate in Complex Environments”, ICLR2017
[Zhu et al., 2017] “Target driven visual navigation in indoor scenes using deep reinforcement learning”, IROS2017
[Zhu et al., 2017]
This Work
6. 貢献3:Without Explicit Maps
6
[Parisotto et al., 2018] This Work
[Parisotto et al., 2018] “Neural Map: Structured Memory For Deep Reinforcement Learning”, ICLR2018
17. Experiments
1. GoalNav vs. CityNav
2. Generalization for Unseen Goal
3. Transferability
4. Ablation Study
17
18. GaolNav vs. CityNav
1. GoalNav vs. CityNav
1. Oracleは最短経路
2. Heuristicはランダム
3. CityNavの方が安定かつ精度良い
4. Skipありが単一都市では良い
2. Generalization for Unseen Goal
3. Transferability
4. Ablation Study
18
New York
London
19. Generalization for Unseen Goal
1. GoalNav vs. CityNav
2. Generalization for Unseen Goal
1. 25%の区画を訓練時ゴールに指定しない(上
図黒部分)
Coarse: 1km×1km, Medium: 0.5km×0.5km,
Fine: 0.25km×0.25km
2. 大きく削ると精度劣化
3. ゴールまで半分の位置への
到達は変化少ない (T1/2)
3. Transferability
4. Ablation Study 19
20. Transferability
1. GoalNav vs. CityNav
2. Generalization for Unseen Goal
3. Transferability
– TargetはWall Street、
訓練はそれ以外の3~5区画
– (a) Target Only, (b) Jointly All,
(c) Transfer (Train w/o target -> target))
1. cは学習都市増やすと精度上がる
2. 5区画使った場合はbとcがcomparable
(と主張しているが…?)
3. 転移する時はSkipしないほうが精度高
(Policy LSTMの入力がそろうから)
4. Ablation Study
20
23. “Leaning to Navigate in Cities Without a Map”, arXiv
• 余裕あったら
23
Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis
Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell (DeepMind)