  Despite advancements in robotic-assisted surgery, automating complex tasks
like suturing remain challenging due to the need for adaptability and
precision. Learning-based approaches, particularly reinforcement learning (RL)
and imitation learning (IL), require realistic simulation environments for
efficient data collection. However, current platforms often include only
relatively simple, non-dexterous manipulations and lack the flexibility
required for effective learning and generalization.
  We introduce SurgicAI, a novel platform for development and benchmarking
addressing these challenges by providing the flexibility to accommodate both
modular subtasks and more importantly task decomposition in RL-based surgical
robotics. Compatible with the da Vinci Surgical System, SurgicAI offers a
standardized pipeline for collecting and utilizing expert demonstrations. It
supports deployment of multiple RL and IL approaches, and the training of both
singular and compositional subtasks in suturing scenarios, featuring high
dexterity and modularization. Meanwhile, SurgicAI sets clear metrics and
benchmarks for the assessment of learned policies. We implemented and evaluated
multiple RL and IL algorithms on SurgicAI. Our detailed benchmark analysis
underscores SurgicAI's potential to advance policy learning in surgical
robotics. Details: \url{https://github.com/surgical-robotics-ai/SurgicAI
