SDSRA: A Skill-Driven Skill-Recombination Algorithm for Efficient Policy Learning
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Abstract (arXiv)
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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References · 8
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2016OpenAI Gymcited by 1,684
- 2019Dynamics-Aware Unsupervised Discovery of Skillscited by 308
- 2021Goal-Conditioned Reinforcement Learning with Imagined Subgoalscited by 129
- 2018On Learning Intrinsic Rewards for Policy Gradient Methodscited by 100
- 2019A survey on intrinsic motivation in reinforcement learningcited by 79
- 2022CIC: Contrastive Intrinsic Control for Unsupervised Skill Discoverycited by 66
- 2022ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Rewardcited by 11
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