图 书 馆
神经刺激政策基于强化学习模型的流形嵌入Manifold Embeddings for Model-Based Reinforcement Learning of Neurostimulation Policies |
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| 课程网址: | http://videolectures.net/icml09_bush_membrlnp/ |
| 主讲教师: | Keith Bush |
| 开课单位: | 阿利坎特大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Real-world reinforcement learning problems often exhibit nonlinear, continuous-valued, noisy, partially-observable state-spaces that are prohibitively expensive to explore. The formal reinforcement learning framework, unfortunately, has not been successfully demonstrated in a real-world domain having all of these constraints. We approach this domain with a two-part solution. First, we overcome continuous-valued, partially observable state-spaces by constructing manifold embeddings of the system’s underlying dynamics, which substitute as a complete state-space representation. We then define a generative model over this manifold to learn a policy off-line. The model-based approach is preferred because it enables simplification of the learning problem by domain knowledge. In this work we formally integrate manifold embeddings into the reinforcement learning framework, summarize a spectral method for estimating embedding parameters, and demonstrate the model-based approach in a complex domain-adaptive seizure suppression of an epileptic neural system. |
| 关 键 词: | 强化学习; 流形嵌入; 神经系统 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-07：lxf |
| 阅读次数: | 65 |