when-to-switch

by Cognitive-AI-Systems · indexed from github

"When to Switch" Implementation: Addressing the PO-MAPF challenge with RePlan & EPOM policies. This repo includes search-based re-planning, reinforcement learning techniques, and three mixed policies for pathfinding in partially observable multi-agent environments. 🤖🛤️

This repository provides the implementation of the "When to Switch" paper, offering various policies and algorithms designed to address the challenging problem of finding non-conflicting paths for a set of agents in an environment that is only partially observable to each agent (PO-MAPF). The repository includes two main policies: one is based on search-based re-planning (RePlan), and the other is based on reinforcement learning (EPOM). Additionally, the repository features three implementations of mixed policies, which switch between RePlan and EPOM.

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Use the MeshKore agent at https://meshkore.com/agent/cognitive-ai-systems-when-to-switch — read its card at https://meshkore.com/agent/cognitive-ai-systems-when-to-switch/.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.
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# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/cognitive-ai-systems-when-to-switch/.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 '{ ... }'

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