DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 신진우 | - |
dc.contributor.author | Son, Kyunghwan | - |
dc.contributor.author | 손경환 | - |
dc.date.accessioned | 2024-08-08T19:31:39Z | - |
dc.date.available | 2024-08-08T19:31:39Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1100076&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/322170 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2024.2,[vi, 65 p. :] | - |
dc.description.abstract | In cooperative multi-agent reinforcement learning, the outcomes of agent-wise policies are highly stochastic due to the two sources of risk: (a) random actions taken by teammates and (b) random transition and rewards. Although the two sources have very distinct characteristics, existing frameworks are insufficient to control the risk-sensitivity of agent-wise policies in a disentangled manner. To this end, we propose Disentangled RIsk-sensitive Multi-Agent reinforcement learning (DRIMA) to separately access the risk sources. For example, our framework allows an agent to be optimistic with respect to teammates (who can prosocially adapt) but more risk-neutral with respect to the environment (which does not adapt). Our experiments demonstrate that DRIMA significantly outperforms prior state-of-the-art methods across various scenarios in the StarCraft Multi-agent Challenge environment. Notably, DRIMA shows robust performance where prior methods learn only a highly suboptimal policy, regardless of reward shaping, exploration scheduling, and noisy (random or adversarial) agents. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 기계학습▼a심층학습▼a강화학습▼a다중-에이전트 강화학습 | - |
dc.subject | Machine learning▼aDeep learning▼aReinforcement learning▼aMulti-agent reinforcement learning | - |
dc.title | Factored value functions for cooperative multi-agent reinforcement learning | - |
dc.title.alternative | 협력 다중 에이전트 강화 학습을 위한 가치 분리 함수 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Shin, Jinwoo | - |
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