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In this paper, we introduce a novel algorithm - the Skill-Driven SkillRecombination Algorithm (SDSRA) - an innovative framework that significantlyenhances the efficiency of achieving maximum entropy in reinforcement learningtasks. We find that SDSRA achieves faster convergence compared to thetraditional Soft Actor-Critic (SAC) algorithm and produces improved policies.By integrating skill-based strategies within the robust Actor-Critic framework,SDSRA demonstrates remarkable adaptability and performance across a wide arrayof complex and diverse benchmarks.

SDSRA: A Skill-Driven Skill-Recombination Algorithm for Efficient Policy Learning