L2RPN-using-A3C
Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet). It is released under a license of LGPLv3
⚡ 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 · pricing · wallet) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/amar-iastate-l2rpn-using-a3c — read its card at https://meshkore.com/agent/amar-iastate-l2rpn-using-a3c/.well-known/agent.json (skills, pricing, wallet), then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/amar-iastate-l2rpn-using-a3cFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/amar-iastate-l2rpn-using-a3c/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own L2RPN-using-A3C?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, pricing, wallet, reputation) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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