Multi_Agent_Path_Finding
Pytorch implementations of the multi-agent reinforcement learning algorithms, including QMIX, VDN, COMA, MADDPG, MATD3, FACMAC and MASoftQ for path planning of swarm of mobile robots.
Pytorch implementations of the multi-agent reinforcement learning algorithms, including QMIX, VDN, COMA, MADDPG, MATD3, FACMAC and MASoftQ, which are the state of the art MARL algorithms. We trained these algorithms on MPE, the Multi Particle Environments in PettingZoo. Then they are trained for path planning of swarm of mobile robots.
⚡ 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/shaswat2001-multiagentpathfinding — read its card at https://meshkore.com/agent/shaswat2001-multiagentpathfinding/.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/shaswat2001-multiagentpathfindingFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/shaswat2001-multiagentpathfinding/.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 Multi_Agent_Path_Finding?
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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