TycoonLE: A Jax reinforcement learning environment for long-horizon planning

Researchers released TycoonLE, a JAX-based reinforcement learning environment for long-horizon planning in a simulated logistics economy. The environment supports action legality, delayed rewards, and replayable audit traces, with a companion benchmark report at TycoonBench. It is designed to study agent planning and decision-making under economic constraints.

Tycoon Learning Environment TycoonLE is a reinforcement learning environment for economically grounded, long-horizon planning. Agents operate in a simulated logistics economy where they allocate capital, build transport routes, move cargo, manage debt, and optimize delayed returns. It is designed to study action legality, candidate-frontier decision interfaces, financing timing, delayed rewards, procedural variation, and replayable audit traces. TycoonLE uses a fixed-shape interface. Agents choose among valid route, finance, and wait candidates, making rollouts compatible with JAX transformations such as jit , vmap , and scan . The replay UI makes policies inspectable through route choices, cargo flow, financing behavior, reward, score, and profit over time. TycoonBench provides a companion benchmark report for comparing agent and model performance on TycoonLE planning tasks: vrtnis.github.io/tycoonbench https://vrtnis.github.io/tycoonbench/ . Use Python 3.11 or 3.12: py -3.12 -m venv .venv .\.venv\Scripts\python.exe -m pip install -e ". test " npm install python import jax from tycoonle jax import TycoonLE env = TycoonLE split="dev", family="chain" state, timestep = env.reset jax.random.PRNGKey 0 action = timestep.observation.action mask.argmax state, timestep = env.step state, action Export a replay: .\.venv\Scripts\python.exe examples\quickstart.py npm run dev Open the browser UI and load runs/quickstart/replay.json . Run tests: .\.venv\Scripts\python.exe -m pytest npm run build Run a small PPO smoke train: .\.venv\Scripts\python.exe examples\train ppo jax.py --updates 1 --num-envs 4 --rollout-length 4 --update-epochs 1 --hidden-sizes 32 If you find this work useful, consider citing: @software{tycoonle, title = {TycoonLE}, author = {TycoonLE contributors}, year = {2026}, url = {https://github.com/vrtnis/tycoon-learning-environment} } TycoonLE uses sprite artwork from OpenGFX https://github.com/OpenTTD/OpenGFX , an open-source graphics base set for OpenTTD https://www.openttd.org/ .