  In this paper, we introduce a novel algorithm - the Skill-Driven Skill
Recombination Algorithm (SDSRA) - an innovative framework that significantly
enhances the efficiency of achieving maximum entropy in reinforcement learning
tasks. We find that SDSRA achieves faster convergence compared to the
traditional 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 array
of complex and diverse benchmarks.
