dc-rl
SustainDC is a set of Python environments for Data Center simulation and control using Heterogeneous Multi Agent Reinforcement Learning. Includes customizable environments for workload scheduling, cooling optimization, and battery management, with integration into Gymnasium.
This work builds on our previous research and extends the methodologies and insights gained from our previous work. The original code, referred to as DCRL-Green, can be found in the legacy branch of this repository. For users looking for the original implementation, please visit the legacy branch. The current repository, SustainDC, represents an advanced iteration of DCRL-Green, incorporating enhanced features and improved benchmarking capabilities. This evolution reflects our ongoing commitment to advancing sustainable data center control.
⚡ 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/hewlettpackard-dc-rl — read its card at https://meshkore.com/agent/hewlettpackard-dc-rl/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/hewlettpackard-dc-rlFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/hewlettpackard-dc-rl/.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 dc-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.