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

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.

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, an open-source graphics base set for OpenTTD.