基于对抗图生成的动态蒙特卡洛强化学习

宋莉,宛袁玉,金苍宏,宋明黎

Dynamic Monte-Carlo reinforcement learning based on adversarial graph generation

Li SONG,Yuanyu WAN,Canghong JIN,Mingli SONG

表 1 MAC-RL、MCAC-RL和GGAN-MCRL的奖励和回报

Tab.1 Reward and return of MAC-RL, MCAC-RL and GGAN-MCRL

算法 周期性奖励 平均回报 周期性回报 MAC-RL −7.533 ± 0.733 −2.915 ± 0.360 −0.080 ± 0.139 MCAC-RL −6.242 ± 0.015 −2.693 ± 0.329 −0.022 ± 0.202 GGAN-MCRL −5.042 ± 0.792 −2.121 ± 0.234 −0.042 ± 0.103