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Detail
Journal Article
멀티에이전트 강화학습을 위한 통신 기술 동향
- Issue Date
- 2023-08
- Citation
- 전자통신동향분석, v.38, no.4, pp.104-115
- ISSN
- 1225-6455
- Publisher
- 한국전자통신연구원
- Language
- Korean
- Type
- Journal Article
- Abstract
- Communication for multiagent reinforcement learning (MARL) has emerged to promote understanding of an entire environment. Through communication for MARL, agents can cooperate by choosing the best action considering not only their surrounding environment but also the entire environment and other agents. Hence, MARL with communication may outperform conventional MARL. Many communication algorithms have been proposed to support MARL, but current analyses remain insufficient. This paper presents existing communication algorithms for MARL according to various criteria such as communication methods, contents, and restrictions. In addition, we consider several experimental environments that are primarily used to demonstrate the MARL performance enhanced by communication.
- KSP Keywords
- Communication algorithms, Reinforcement Learning(RL), Surrounding environment, multi-agent reinforcement learning
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
