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

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. Reinforcement Learning (RL) has significantly advanced, with the Soft Actor-Critic (SAC) algorithm, introduced by Haarnoja et al. (2018), standing out for efficient exploration in complex tasks. Despite its strengths, SAC, like other RL methods, faces challenges in more intricate environments.