WRL-Dragon

WRL-Dragon automates reinforcement learning policy development using a hierarchical multi-agent interaction system. Instead of having to manually write and tune RL policies, a CEO agent analyzes environments, Coder agents generate policy code using LLMs, and QA agents run rollouts to evaluate performance. Results feed back into subsequent round, creating an autonomous improve loop across multiple Gym environments simultaneously. The system solves the tedious cycle of write-test-iterate in RL research by letting AI agents handle the entire pipeline from strategy to code generation to evaluation, all while a real-time dashboard lets you watch the process unfold. Beyond this orchestration loop, the system meta-trains smaller child agents directly on RL tasks, combining LLM-driven code generation with traditional RL training to produce increasingly capable policies!