ORLEP: an efficient offline reinforcement learning evaluation platform

Developing offline reinforcement learning evaluation applications faces challenges such as heterogeneous data and algorithm integration, user-friendly interface, and flexible resource management. This paper designs and implements ORLEP, an efficient platform to provide high-level services for offline reinforcement learning evaluation. Besides integrating underlying infrastructure with highly concurrency and reliability, core components with distributed deployment and 3rd party libs and benchmarks incorporation, ORLEP supplies high-level abstractions for (1) data management, (2) model training and evaluation, (3) result visualization, and (4) resource configuration and supervision. Moreover, this paper verifies specific cases and the results demonstrate the performance and scalability of the proposed ORLEP.