Structure-Preserving Imitation Learning With Delayed Reward: An Evaluation Within the RoboCup Soccer 2D Simulation Environment.
deep learning
deep reinforcement learning
end-to-end learning
imitation learning
learning with delayed reward
learning with structure preservation
Journal
Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350
Informations de publication
Date de publication:
2020
2020
Historique:
received:
10
05
2020
accepted:
04
08
2020
entrez:
27
1
2021
pubmed:
28
1
2021
medline:
28
1
2021
Statut:
epublish
Résumé
We describe and evaluate a neural network-based architecture aimed to imitate and improve the performance of a fully autonomous soccer team in RoboCup Soccer 2D Simulation environment. The approach utilizes deep Q-network architecture for action determination and a deep neural network for parameter learning. The proposed solution is shown to be feasible for replacing a selected behavioral module in a well-established RoboCup base team,
Identifiants
pubmed: 33501289
doi: 10.3389/frobt.2020.00123
pmc: PMC7805756
doi:
Types de publication
Journal Article
Langues
eng
Pagination
123Informations de copyright
Copyright © 2020 Nguyen and Prokopenko.
Références
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