论文标题
使用动物视频在强化学习中进行导航的机会和挑战
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation
论文作者
论文摘要
我们研究了动物视频（观测）来提高稀疏奖励的导航任务中的增强效率和性能。在理论上的考虑方面，我们利用对非政策RL的加权政策优化，并描述从动物视频中学习时的主要挑战。我们建议解决方案并在一系列2D导航任务上测试我们的想法。我们展示了我们的方法如何利用动物视频来改善不利用此类观察结果的RL算法的性能。
We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a series of 2D navigation tasks. We show how our methods can leverage animal videos to improve performance over RL algorithms that do not leverage such observations.
