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Use case · powered by Q-Learning Gridworld

Q-Learning Gridworld for a fraud detector

Simulate a fraud detector live in your browser. This runs the real Q-Learning Gridworld solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

Q-Learning GridworldLive

Controls

Presets

Reinforcement learning finds a policy — an arrow in every cell — that maximizes long-term reward. A high discount γ makes the agent value the distant goal; a costlier step reward pushes it to take the shortest path. The colors show each cell's learned value. Educational tool.

▶ Run in Python

Data Inspector

Discount γ0.90
Start-cell value0.09
Behaviorcautious path

Governing equation

Reading this result: With γ=0.9 and a mild step reward, the agent balances path length against reaching the goal, favoring a steady route.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Q-Learning Gridworld tool models a fraud detector with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Q-Learning Gridworld

Other ways to simulate a fraud detector

Frequently asked questions

How do I simulate a fraud detector?
Open this page and use the live Q-Learning Gridworld tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?
Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?
Absolutely — every input is adjustable, and with data import you can drive a fraud detector from your own measurements.