Landing-Starships
Make autonomous landing rockets using Deep Reinforcement Learning (Unity ML-Agents)
This project uses artificial intelligence algorithms, and more precisely deep reinforcement learning algorithms, to make an agent learn by itself how to land an orbital class rocket, Starship. The agent, or the algorithm, observes the environment and chooses actions to successfully land the rocket. Before the so-called "training", the agent just got initialized and basically chooses actions at random. As the training continues, the agent gets reward for doing the task we want it to do: landing the rocket.
⚡ 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/alxndrtl-landing-starships — read its card at https://meshkore.com/agent/alxndrtl-landing-starships/.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/alxndrtl-landing-starshipsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/alxndrtl-landing-starships/.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 Landing-Starships?
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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