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

Tycoon Learning Environment

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

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