Towards High Efficient Long-Horizon Planning With Expert-Guided Motion-Encoding Tree Search

Author:

Zhou Tong1ORCID,Lyu Erli2ORCID,Cen Guangdu3ORCID,Zha Ziqi3ORCID,Qi Senmao3ORCID,Wang Jiaole3ORCID,Meng Max Q.-H.4ORCID

Affiliation:

1. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China

2. Faculty of Applied Science, Macao Polytechnic University, Macau, China

3. School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen, China

4. Shenzhen Key Laboratory of Robotics Perception and Intelligence, Southern University of Science and Technology, Shenzhen, China

Funder

Talent Recruitment Project of Guangdong

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference24 articles.

1. Grandmaster level in StarCraft II using multi-agent reinforcement learning

2. From motor control to team play in simulated humanoid football

3. Mastering Atari, Go, chess and shogi by planning with a learned model;Schrittwieser,2019

4. Vector quantized models for planning;Ozair,2021

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