DSS-SimPy-RL
This repository is an Reinforcement Learning Platform for learning agents to control cyber-physical Power Distribution Systems resiliently. The cyber environment in based on SimPy Discrete Event Simulator, while the distribution system is backened by Open-DSS.
Use the environment.yaml file to create the conda environment for installing the python packages required in this repository. Commad: conda env create --file environment.yaml
⚡ 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/natlabrockies-dss-simpy-rl — read its card at https://meshkore.com/agent/natlabrockies-dss-simpy-rl/.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/natlabrockies-dss-simpy-rlFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/natlabrockies-dss-simpy-rl/.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 DSS-SimPy-RL?
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.