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Information × Registration Number 2122U003011, Article popup.category Препринт Title popup.author Nahirnyi Oleksii popup.publication 01-01-2022 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/3165 popup.publisher Description Solving sparse-reward environments is one of the most considerable challenges for state-of-the-art (SOTA) Reinforcement Learning (RL). Recent usage of sparse-rewards in procedurally-generated environments (PGE) to more adequately measure agent’s generalization capabilities via randomization makes this challenge even harder. Despite some progress of newly created exploration-based algorithms in MiniGrid PGEs, the task remains open for research in terms of improving sample complexity. We contribute to solving this task by creating a new formulation of exploratory intrinsic reward. We base this formulation on a thorough review and categorization of other methods in this area. Agent that optimizes an RL objective with such a formulation performs better than SOTA methods in some small or medium sized PGEs. popup.nrat_date 2025-05-09 Close