The detailed results are described at GitHub (in English):
https://github.com/jkatsuta/exp-18-1q
(maddpg/experiments/my_notes/のexp1 ~ exp6)
立教大学のセミナー資料(前篇)です。
資料後篇:
https://www.slideshare.net/JunichiroKatsuta/ss-108099542
ブログ(動画あり):
https://recruit.gmo.jp/engineer/jisedai/blog/multi-agent-reinforcement-learning/
The detailed results are described at GitHub (in English):
https://github.com/jkatsuta/exp-18-1q
(maddpg/experiments/my_notes/のexp1 ~ exp6)
立教大学のセミナー資料(前篇)です。
資料後篇:
https://www.slideshare.net/JunichiroKatsuta/ss-108099542
ブログ(動画あり):
https://recruit.gmo.jp/engineer/jisedai/blog/multi-agent-reinforcement-learning/
XRミーティング 2022/06/15【AR/CR/MR/SR/VR】(https://osaka-driven-dev.connpass.com/event/250007/)
Mixed Reality Dev Days 2022でpublic previewとなったMRTK3のプロジェクト環境を作り方について。
手順は
Qiita(https://qiita.com/miyaura/items/df5947d45cb3b86bbf18)
The document discusses constraint motion planning, which is a type of motion planning that takes constraints into account. It provides an example where the constraint is that an object must remain on the surface of a sphere. Directly searching the full space is not possible, so instead the constrained space is approximated using a first-order Taylor expansion, though this introduces approximation errors. It then provides examples of how to implement constrained motion planning. Finally, it mentions that constrained planning is used in the MoveIt motion planning framework and provides a tutorial link for learning more about constrained planning in the OMPL library.