@inproceedings{oai:oist.repo.nii.ac.jp:00001423,
 author = {Han, Dongqi},
 book = {第 28 回 日本神経回路学会全国大会 講演論文集, The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society},
 month = {Oct},
 note = {Reinforcement learning is a useful ap-proach to solve machine learning problems by self-exploration when training samples are not provided.However, researchers usually ignore the importance ofthe choice of exploration noise. In this paper, I showthat temporally self-correlated exploration stochastic-ity, generated by Ornstein-Uhlenbeck process, can sig-nificantly enhance the performance of reinforcementlearning tasks by improving exploration.},
 pages = {54--55},
 publisher = {Japanese Neural Network Society},
 title = {Improving exploration in reinforcement learning with temporally correlated stochasticity},
 year = {2018}
}