Paper ID: 2209.10081

Revisiting Discrete Soft Actor-Critic

Haibin Zhou, Tong Wei, Zichuan Lin, junyou li, Junliang Xing, Yuanchun Shi, Li Shen, Chao Yu, Deheng Ye

We study the adaption of Soft Actor-Critic (SAC), which is considered as a state-of-the-art reinforcement learning (RL) algorithm, from continuous action space to discrete action space. We revisit vanilla discrete SAC and provide an in-depth understanding of its Q value underestimation and performance instability issues when applied to discrete settings. We thereby propose Stable Discrete SAC (SDSAC), an algorithm that leverages entropy-penalty and double average Q-learning with Q-clip to address these issues. Extensive experiments on typical benchmarks with discrete action space, including Atari games and a large-scale MOBA game, show the efficacy of our proposed method. Our code is at: this https URL

Submitted: Sep 21, 2022