Co-Evolving Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation-Reference-Cited by-同舟云学术

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Co-Evolving Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation

Published:2024 Issue: Volume:12 Page:99439-99451
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Siddiqua Ayesha¹^ORCID,Liu Siming¹^ORCID,Siddika Nipu Ayesha²,Harris Anthony¹,Liu Yan³^ORCID

Affiliation:

1. Department of Computer Science, Missouri State University, Springfield, MO, USA

2. Department of Computer Science and Software Engineering, University of Wisconsin-Platteville, Platteville, WI, USA

3. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/6287639/10380310/10600472-aam.pdf

Reference42 articles.

1. Human-level control through deep reinforcement learning

2. Move prediction in Gomoku using deep learning

3. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

4. Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

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