Unity_ML_Agents
Unity ML-agents Project Repository of RLKorea
이 레포지토리는 Reinforcement Learning Korea의 Unity ML-agents 튜토리얼 프로젝트를 위한 레포입니다. 이 레포는 유니티 ML-Agents(Github)로 만든 간단한 환경들을 제공합니다. 또한 제공된 환경들에서 에이전트를 학습할 수 있는 심층강화학습 알고리즘을 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-unitymlagents — read its card at https://meshkore.com/agent/reinforcement-learning-kr-unitymlagents/.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-unitymlagentsFor 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-unitymlagents/.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?
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.