Hierarchical reinforcement learning from imperfect demonstrations through reachable coverage-based subgoal filtering-Reference-Cited by-同舟云学术

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Hierarchical reinforcement learning from imperfect demonstrations through reachable coverage-based subgoal filtering

Published:2024-06 Issue: Volume:294 Page:111736
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Tang Yu^ORCID,Guo Shangqi^ORCID,Liu Jinhui^ORCID,Wan Bo,An Lingling,Liu Jian K.

Funder

Natural Science Foundation of Guangdong Province

National Postdoctoral Program for Innovative Talents

Key Technology Research and Development Program of Shandong Province

Science and Technology Planning Project of Guangdong Province

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference61 articles.

1. Mastering complex control in moba games with deep reinforcement learning;Ye,2020

2. Mastering the game of go with deep neural networks and tree search;Silver;Nature,2016

3. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play;Silver;Science,2018

4. Grandmaster level in StarCraft II using multi-agent reinforcement learning;Vinyals;Nature,2019

5. Dota 2 with large scale deep reinforcement learning;Berner,2019

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