图 书 馆
逆强化学习中奖励估计的主动学习Active Learning for Reward Estimation in Inverse Reinforcement Learning |
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| 课程网址: | https://videolectures.net/videos/ecmlpkdd09_melo_alreirl |
| 主讲教师: | Francisco S. Melo |
| 开课单位: | 信息不详。欢迎您在右侧留言补充。 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, we introduce active learning for inverse reinforcement learning. We propose an algorithm that allows the agent to query the demonstrator for samples at specific states, instead of relying only on samples provided at ”arbitrary” states. The purpose of our algorithm is to estimate the reward function with similar accuracy as other methods from the literature while reducing the amount of policy samples required from the expert. We also discuss the use of our algorithm in higher dimensional problems, using both Monte Carlo and gradient methods. We present illustrative results of our algorithm in several simulated examples of different complexities. |
| 关 键 词: | 蒙特卡罗; 梯度方法; 逆强化学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-04-21：zsp |
| 最后编审: | 2025-04-21：zsp |
| 阅读次数: | 2 |