Unity_ML_Agents_2.0
Repository for implementing Unity ML-Agents 2.0
이 레포지토리는 Reinforcement Learning Korea의 유니티 머신러닝 에이전트 튜토리얼 제작 프로젝트의 결과로 제작된 강의들을 위한 자료들을 포함하고 있습니다. 본 레포에서는 다음의 강의에 대한 자료를 제공합니다.
⚡ 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/reinforcement-learning-kr-unitymlagents20 — read its card at https://meshkore.com/agent/reinforcement-learning-kr-unitymlagents20/.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/reinforcement-learning-kr-unitymlagents20For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/reinforcement-learning-kr-unitymlagents20/.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 Unity_ML_Agents_2.0?
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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.