rl-agent-based-traffic-control
Develop agent-based traffic management system by model-free reinforcement learning
Traffic congestion is always a daunting problem that affects people's daily life across the world. The objective of this work is to develop an intelligent traffic signal management to improve traffic performance, including alleviating traffic congestion, reducing waiting times, improving the throughput of a road network, and so on. Traditionally, traffic signal control typically formulates signal timing as an optimization problem.
⚡ 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/matlab-deep-learning-rl-agent-based-traffic-control — read its card at https://meshkore.com/agent/matlab-deep-learning-rl-agent-based-traffic-control/.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/matlab-deep-learning-rl-agent-based-traffic-controlFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/matlab-deep-learning-rl-agent-based-traffic-control/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Do you own rl-agent-based-traffic-control?
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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