2017
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
NeurIPS 2017poster
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state s…