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DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learning.

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Názov: DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learning.
Autori: MA, XIAOTENG, CHEN, JUNYAO, XIA, LI, YANG, JUN, ZHAO, QIANCHUAN, ZHOU, ZHENGYUAN
Zdroj: Journal of Artificial Intelligence Research; 2025, Vol. 83, p1-28, 28p
Predmety: REINFORCEMENT learning, ENTROPY, ALGORITHMS
Abstrakt: We present Distributional Soft Actor-Critic (DSAC), a distributional reinforcement learning (RL) algorithm that combines the strengths of distributional information of accumulated rewards and entropy-driven exploration from Soft Actor-Critic (SAC) algorithm. DSAC models the randomness in both action and rewards, surpassing baseline performances on various continuous control tasks. Unlike standard approaches that solely maximize expected rewards, we propose a unified framework for risk-sensitive learning, one that optimizes the risk-related objective while balancing entropy to encourage exploration. Extensive experiments demonstrate DSAC's effectiveness in enhancing agent performances for both risk-neutral and risk-sensitive control tasks. [ABSTRACT FROM AUTHOR]
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Databáza: Complementary Index
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Abstrakt:We present Distributional Soft Actor-Critic (DSAC), a distributional reinforcement learning (RL) algorithm that combines the strengths of distributional information of accumulated rewards and entropy-driven exploration from Soft Actor-Critic (SAC) algorithm. DSAC models the randomness in both action and rewards, surpassing baseline performances on various continuous control tasks. Unlike standard approaches that solely maximize expected rewards, we propose a unified framework for risk-sensitive learning, one that optimizes the risk-related objective while balancing entropy to encourage exploration. Extensive experiments demonstrate DSAC's effectiveness in enhancing agent performances for both risk-neutral and risk-sensitive control tasks. [ABSTRACT FROM AUTHOR]
ISSN:10769757
DOI:10.1613/jair.1.17526