The advancement of deep learning significantly accelerates the development of future integrated sensing and communication (ISAC) systems. Deep reinforcement learning (DRL), as a promising deep learning approach, has emerged to leverage a distributed personalized dataset from different reconfigurable intelligent surface (RIS) nodes. However, the high costs associated with data offloading and model training pose challenges to implementing network intelligence within existing ISAC frameworks, particularly at network edges. To address this limitation, a paradigm of RIS-enabled DRL technology is developed, which can overcome the arithmetic, high frequency transmission and coverage region problems. The fundamental studies with respect to the RIS-assisted ISAC modeling and its solution are investigated, which can provide insights into the design of RIS-enabled DRL in ISAC network. To facilitate the corresponding implementation, key techniques are proposed to integrate the communication, sensing and computation capabilities of ISAC network. Moreover, future trends of RIS-enabled DRL technology for ISAC network, such as potential applications and open issues, are discussed.