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

2023-12-06Code Available0· sign in to hype

Eric H. Jiang, Andrew Lizarraga

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Abstract

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.

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