TycoonLE: A Jax reinforcement learning environment for long-horizon planning (github.com) AI

TycoonLE is a JAX-based reinforcement learning environment for economically grounded, long-horizon planning in a simulated logistics/transport economy, where agents allocate capital, build routes, move cargo, manage debt, and optimize delayed rewards. The repo emphasizes action legality and uses a fixed-shape interface designed to work with JAX transformations (e.g., jit/vmap/scan), along with an audit/replay UI to inspect route choices, cargo flows, financing behavior, and profit over time. It also includes TycoonBench, a companion benchmark report for comparing performance on TycoonLE planning tasks.