Automated design of action advising trigger conditions for multiagent reinforcement learning: A genetic programming-based approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{WANG:2024:swevo,
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author = "Tonghao Wang and Xingguang Peng and Tao Wang and
Tong Liu and Demin Xu",
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title = "Automated design of action advising trigger conditions
for multiagent reinforcement learning: A genetic
programming-based approach",
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journal = "Swarm and Evolutionary Computation",
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volume = "85",
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pages = "101475",
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year = "2024",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2024.101475",
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URL = "https://www.sciencedirect.com/science/article/pii/S2210650224000087",
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keywords = "genetic algorithms, genetic programming, Multiagent
reinforcement learning, Action advising, Multiagent
systems",
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abstract = "Action advising is a popular and effective approach to
accelerating independent multiagent reinforcement
learning (MARL), especially for the learning systems
that all the agents learn from scratch and the roles of
them (advisors or advisees) cannot be predefined. The
key component of action advising is the trigger
condition, which answers the question of when to
advise. Previous works mainly focus on the design of
novel trigger conditions manually; however, since those
conditions are often designed heuristically, the
performance may be affected by the preference of the
designers. To this end, this paper tries to solve the
action advising problem automatically using genetic
programming (GP), an evolutionary computation
technique. A framework incorporating GP to action
advising is provided, together with a novel population
initialization method to enhance the performance.
Empirical studies are provided to demonstrate the
effectiveness of the proposed framework. More
importantly, thanks to the high transparency of GP,
comprehensive analysis is also conducted based on the
results. Interesting and inspiring insights to the
action advising problem are condensed from the
discussions, which may provide guidance to future
works",
- }
Genetic Programming entries for
Tonghao Wang
Xingguang Peng
Tao Wang
Tong Liu
Demin Xu
Citations