What’s problem?
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). Count-based exploration
阅读背景：
PR10.10:#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
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