TycoonLE: A Jax reinforcement learning environment for long-horizon planning
Source: Hacker News
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.
Install
Use Python 3.11 or 3.12:
py -3.12 -m venv .venv ..venv\Scripts\python.exe -m pip install -e ”.[test]” npm install
Quickstart
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
Training
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
Citation
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} }
Artwork Credits
TycoonLE uses sprite artwork from OpenGFX, an open-source graphics base set for OpenTTD.