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

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.