图 书 馆
策略搜索:方法和应用Policy Search: Methods and Applications |
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| 课程网址: | http://videolectures.net/icml2015_neumann_peters_policy_search/ |
| 主讲教师: | Jan Peters; Gerhard Neumann |
| 开课单位: | 达姆施塔特工业大学计算机科学系 |
| 开课时间: | 2015-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Policy search is a subfield in reinforcement learning which focuses on finding good parameters for a given policy parametrization. It is well suited for robotics as it can cope with high-dimensional state and action spaces, one of the main challenges in robot learning. We review recent successes of both model-free and model-based policy search in robot learning. Model-free policy search is a general approach to learn policies based on sampled trajectories. We classify model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and present a unified view on existing algorithms. Learning a policy is often easier than learning an accurate forward model, and, hence, model-free methods are more frequently used in practice. How- ever, for each sampled trajectory, it is necessary to interact with the robot, which can be time consuming and challenging in practice. Model-based policy search addresses this problem by first learning a simulator of the robot’s dynamics from data. Subsequently, the simulator generates trajectories that are used for policy learning. For both model- free and model-based policy search methods, we review their respective properties and their applicability to robotic systems. |
| 关 键 词: | 策略搜索; 强化学习; 高维状态; 策略评估 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-24：chenxin01 |
| 最后编审: | 2023-05-18：chenxin01 |
| 阅读次数: | 32 |