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Revisiting Discrete Soft Actor-Critic. (arXiv:2209.10081v1 [cs.LG])
Sept. 22, 2022, 1:11 a.m. | Haibin Zhou, Zichuan Lin, Junyou Li, Deheng Ye, Qiang Fu, Wei Yang
cs.LG updates on arXiv.org arxiv.org
We study the adaption of soft actor-critic (SAC)from continuous action space
to discrete action space. We revisit vanilla SAC and provide an in-depth
understanding of its Q value underestimation and performance instability issues
when applied to discrete settings. We thereby propose 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:https://github.com/coldsummerday/Revisiting-Discrete-SAC.
More from arxiv.org / cs.LG updates on arXiv.org
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