Deep Reinforcement Learning in Multi-End Games
Cited 0 times inCited 0 times in
- Deep Reinforcement Learning in Multi-End Games
- Other Titles
- 멀티 엔드 게임에서의 깊은 강화학습
- Kim, Sol A
- Kim, Kwang In
- 강화학습; 커널회귀; 몬테카를로트리탐색; 컬링; 딥러닝
- Issue Date
- Graduate School of UNIST
- Recently deep reinforcement learning (DRL) algorithms show super human performances in the simulated game domains. In practical points, the sample efficiency is also one of the most important measures to determine the performance of a model. Especially for the environment of large search spaces (e.g. continuous action space), it is very critical condition to achieve the state-of-the-art performance.
In this thesis, we design a model to be applicable to multi-end games in continuous space with high sample efficiency. A multi-end game has several sub-games which are independent each other but affect the result of the game by some rules of its domain. We verify the algorithm in the environment of simulated curling.
- Department of Computer Science and Engineering
- Go to Link;
- Appears in Collections:
- Files in This Item:
Deep Reinforcement Learning in Multi-End Games.pdf
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.