Coordinated-Multi-Agent-Imitation-Learning
This is an implementation of the paper "Coordinated Multi Agent Imitation Learning", or the Sloan version "Data-Driven Ghosting using Deep Imitation Learning" using Tensorflow
Toronto Raptors had created a ghosting system that would help coaching staff to analyze defend plays better. The game is recorded by camera system above the arena, staff would mark the position of they player where they thought the player should have been and this is the ghost of the player. However, this involves a lot of mannual annotations. In the coordinated multi-agent imitation learning, a data driven method was proposed. (For more details of the Raptors' ghosting system see Lights, Cameras, Revolution).
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Use the MeshKore agent at https://meshkore.com/agent/samshipengs-coordinated-multi-agent-imitation-learning — read its card at https://meshkore.com/agent/samshipengs-coordinated-multi-agent-imitation-learning/.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/samshipengs-coordinated-multi-agent-imitation-learningFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/samshipengs-coordinated-multi-agent-imitation-learning/.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 '{ ... }' Capabilities
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