This repository collects and organizes academic papers on exploration techniques in reinforcement learning, covering both classic algorithms and recent frontier research. Papers are organized chronologically by top machine learning conferences (NeurIPS, ICML, ICLR) and categorized by exploration strategy type, including sparse-reward environments and hard-exploration problems. The list serves as a reference for researchers studying the exploration-exploitation trade-off in RL.