图 书 馆
在罕见事件的存在下强化学习Reinforcement Learning in the Presence of Rare Events |
|
| 课程网址: | http://videolectures.net/icml08_frank_rlp/ |
| 主讲教师: | Jordan Frank |
| 开课单位: | 麦吉尔大学 |
| 开课时间: | 2008-08-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the task of reinforcement learning in an environment in which rare significant events occur independently of the actions selected by the controlling agent. If these events are sampled according to their natural probability of occurring, convergence of standard reinforcement learning algorithms is likely to be very slow, and the learning algorithms may exhibit high variance. In this work, we assume that we have access to a simulator, in which the rare event probabilities can be artificially altered. Then, importance sampling can be used to learn with this simulation data. We introduce algorithms for policy evaluation, both using tabular and function approximation representation of the value function. We prove that in both cases, the reinforcement learning algorithms converge. In the tabular case, we also analyze the bias and variance of our approach compared to TD-learning. We evaluate empirically the performance of the algorithm on random Markov Decision Processes, as well as on a large network planning task. |
| 关 键 词: | 强化学习; 自然概率; 政策评估; 马尔可夫决策 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-06：lxf |
| 阅读次数: | 43 |