ML-Agents-with-Google-Colab
Train reinforcement learning agent using ML-Agents with Google Colab.
After struggling, on a particular question (How to run ML-Agents on Google Colab?) for days, I thought it would be great to create a github repo to share my findings on this topic as there is little to no information on the internet. This repo gives information on the above question by testing an example environment on colab. (This example environment is taken from ML-Agents repo)
⚡ 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/dhyeythumar-ml-agents-with-google-colab — read its card at https://meshkore.com/agent/dhyeythumar-ml-agents-with-google-colab/.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/dhyeythumar-ml-agents-with-google-colabFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/dhyeythumar-ml-agents-with-google-colab/.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 ML-Agents-with-Google-Colab?
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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