For Researchers · Q-Learning Gridworld
Q-Learning Gridworld for a pathfinding AI
Built for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model. Simulate a pathfinding AI live below — adjust the inputs and watch it respond, right in your browser.
Q-Learning GridworldLive
learning to reach a goal
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.
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.
More with Q-Learning Gridworld
Q-Learning Gridworld for a neural networkQ-Learning Gridworld for a sorting algorithmQ-Learning Gridworld for a search treeQ-Learning Gridworld for a hash tableQ-Learning Gridworld for a recommendation engineQ-Learning Gridworld for an image classifierQ-Learning Gridworld for a chatbotQ-Learning Gridworld for a game AI
Frequently asked questions
- Is this good for researchers?
- Yes — this version of "Q-Learning Gridworld for a pathfinding AI" is framed for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model.
- Do I need to install anything?
- No. It runs in any modern browser, free, with no account required.