Phasic Diversity Optimization for Population-Based Reinforcement Learning
Jingcheng Jiang,Haiyin Piao,Yu Fu,Yihang Hao,Chuanlu Jiang,Ziqi Wei,Xin Yang,Jingcheng Jiang,Haiyin Piao,Yu Fu,Yihang Hao,Chuanlu Jiang,Ziqi Wei,Xin Yang
Reviewing the previous work of diversity Reinforcement Learning, diversity is often obtained via an augmented loss function, which requires a balance between reward and diversity. Generally, diversity optimization algorithms use Multi-armed Bandits algorithms to select the coefficient in the pre-defined space. However, the dynamic distribution of reward signals for MABs or the conflict between qua...
