simple-mario-game
A classic Mario-style platformer built with JavaFX, featuring an integrated Reinforcement Learning (RL) model developed in Python. This project explores game development and AI, demonstrating how an RL agent can learn to play the game. Contributions are welcome!
Welcome to the Mario JavaFX Game! This is a simple Mario-style platformer built with Java 11 and JavaFX. Enjoy classic gameplay, collect coins, avoid enemies, and reach the flag!
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/mende237-simple-mario-game — read its card at https://meshkore.com/agent/mende237-simple-mario-game/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/mende237-simple-mario-gameFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/mende237-simple-mario-game/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Do you own simple-mario-game?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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