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Dynamics-aware novelty search with behavior repulsion
2022
Proceedings of the Genetic and Evolutionary Computation Conference
Searching solutions for the task with sparse or deceptive rewards is a fundamental problem in Evolutionary Algorithms (EA) and Reinforcement Learning (RL). Existing methods in RL have been proposed to enhance the exploration by encouraging agents to obtain novel states. However, solely seeking a single local optimal solution could be insufficient for the tasks with the deceptive local optima. Novelty-Search (NS) and Quality-Diversity (QD) have shown promising results for finding diverse
doi:10.1145/3512290.3528761
fatcat:ocwbnxzqana4xazfctsr6gezbm