streetlearn
A C++/Python implementation of the StreetLearn environment based on images from Street View, as well as a TensorFlow implementation of goal-driven navigation agents solving the task published in “Learning to Navigate in Cities Without a Map”, NeurIPS 2018
This repository contains an implementation of the StreetLearn C++ engine and Python environment for training navigation agents in real-world photographic street environments, as well as code for implementing the agents used in [1] "Learning to Navigate in Cities Without a Map" (NeurIPS 2018). This environment was also used in two follow-up papers: [2] "Cross-View Policy Learning for Street Navigation" (ICCV 2019) and [3] "Learning to follow directions in Street View" (AAAI 2020), as well as in technical report [4] "The StreetLearn Environment and Dataset".
⚡ 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/google-deepmind-streetlearn — read its card at https://meshkore.com/agent/google-deepmind-streetlearn/.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/google-deepmind-streetlearnFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/google-deepmind-streetlearn/.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
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