图 书 馆
蒙特卡罗模拟平衡Monte-Carlo Simulation Balancing |
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| 课程网址: | http://videolectures.net/icml09_silver_mcsb/ |
| 主讲教师: | David Silver |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we introduce the first algorithms for efficiently learning a simulation policy for Monte-Carlo search. Our main idea is to optimise the balance of a simulation policy, so that an accurate spread of simulation outcomes is maintained, rather than optimising the direct strength of the simulation policy. We develop two algorithms for balancing a simulation policy by gradient descent. The first algorithm optimises the balance of complete simulations, using a policy gradient algorithm; whereas the second algorithm optimises the balance over every two steps of simulation. We compare our algorithms to reinforcement learning and supervised learning algorithms for maximising the strength of the simulation policy. We test each algorithm in the domain of 5x5 Computer Go, using a softmax policy that is parameterised by weights for a hundred simple patterns. When used in a simple Monte-Carlo search, the policies learnt by simulation balancing achieved significantly better performance, with half the mean squared error of a uniform random policy, and equal overall performance to a sophisticated Go engine. |
| 关 键 词: | 蒙特卡洛搜索; 策略梯度算法; 随机政策 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-02-04：nkq |
| 阅读次数: | 43 |