Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Baiming Chen Zuxin Liu Jiacheng Zhu Mengdi Xu Wenhao Ding Liang Li Ding Zhao Baiming Chen Zuxin Liu Jiacheng Zhu Mengdi Xu Wenhao Ding Liang Li Ding Zhao
Safety is a critical concern when deploying reinforcement learning agents for realistic tasks. Recently, safe reinforcement learning algorithms have been developed to optimize the agent’s performance while avoiding violations of safety constraints. However, few studies have addressed the nonstationary disturbances in the environments, which may cause catastrophic outcomes. In this paper, we propos...