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Selectively Decentralized Reinforcement Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Selectively Decentralized Reinforcement Learning. |
| 개인저자 | Nguyen, Thanh Minh. |
| 단체저자명 | Purdue University. Computer Sciences. |
| 발행사항 | [S.l.]: Purdue University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 157 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438375369 |
| 학위논문주기 | Thesis (Ph.D.)--Purdue University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Snehasis Mukhopadhyay. |
| 요약 | The main contributions in this thesis include the selectively decentralized method in solving multi-agent reinforcement learning problems and the discretized Markov-decision-process (MDP) algorithm to compute the sub-optimal learning policy in completely unknown learning and control problems. These contributions tackle several challenges in multi-agent reinforcement learning: the unknown and dynamic nature of the learning environment, the difficulty in computing the closed-form solution of the learning problem, the slow learning performance in large-scale systems, and the questions of how/when/to whom the learning agents should communicate among themselves. Through this thesis, the selectively decentralized method, which evaluates all of the possible communicative strategies, not only increases the learning speed, achieves better learning goals but also could learn the communicative policy for each learning agent. Compared to the other state-of-the-art approaches, this thesis's contributions offer two advantages. First, the selectively decentralized method could incorporate a wide range of well-known algorithms, including the discretized MDP, in single-agent reinforcement learning; meanwhile, the state-of-the-art approaches usually could be applied for one class of algorithms. Second, the discretized MDP algorithm could compute the sub-optimal learning policy when the environment is described in general nonlinear format; meanwhile, the other state-of-the-art approaches often assume that the environment is in limited format, particularly in feedback-linearization form. This thesis also discusses several alternative approaches for multi-agent learning, including Multidisciplinary Optimization. In addition, this thesis shows how the selectively decentralized method could successfully solve several real-worlds problems, particularly in mechanical and biological systems. |
| 일반주제명 | Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
