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直接对机器人数据流进行策略排序Direct Policy Ranking with Robot Data Streams |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_akrour_robot/ |
| 主讲教师: | Riad Akrour |
| 开课单位: | 法国萨克莱酒店 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many machine learning approaches in robotics, based on reinforcement learning, inverse optimal control or direct policy learning, critically rely on robot simulators. This paper investigates a simulatorfree direct policy learning, called Preference-based Policy Learning (PPL). PPL iterates a four-step process: the robot demonstrates a candidate policy; the expert ranks this policy comparatively to other ones according to her preferences; these preferences are used to learn a policy return estimate; the robot uses the policy return estimate to build new candidate policies, and the process is iterated until the desired behavior is obtained. PPL requires a good representation of the policy search space be available, enabling one to learn accurate policy return estimates and limiting the human ranking effort needed to yield a good policy. Furthermore, this representation cannot use informed features (e.g., how far the robot is from any target) due to the simulator-free setting. As a second contribution, this paper proposes a representation based on the agnostic exploitation of the robotic log. The convergence of PPL is analytically studied and its experimental validation on two problems, involving a single robot in a maze and two interacting robots, is presented. |
| 关 键 词: | 机器人; 人工智能; 学习能力; 机器人模拟器; 学习行为策略 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-17：cwx |
| 阅读次数: | 97 |