机器学习实验室博士生系列论坛（第二十三期）——Generalisation in Deep Reinforcement Learning
报告人：Yizheng Hu (PKU)
时间：2022-03-09 15:10-16:10
地点：北大理科一号楼1304会议室&腾讯会议 723 1564 5542
Abstract: Generalisation is important for applying Deep Reinforcement Learning (DRL) algorithms into real world, since real world environments are always changing. A policy suitable for real world scenario must be able to handle these changes. "Generalisation in RL" is a class of problem, and "robust RL" reviewed in my previous talk "Adversarial Attacks and Robustness in DRL" is one of a kind.
Recently, Kirk, Robert, et al. systematically reviewed "Generalisation in RL". They divided generalisation methods into three categories: increasing similarity, handling difference, and RL-specific improvements.
This talk will take their review as the main line, and introduce most generalisation RL works that they cite.
