rl_CARLA
Use Reinforcement Learning to train an autonomous driving agent in CARLA Simulator
rl-CARLA =============== The basic idea is using Raw Image as state spaces to train DDPG Agent. The network architecture is quite simple, if you want to know more, you can check here. In order to evaluate the performance of the RL method, we first used supervised learning to train a network as baseline. Then we investigate the performance of RL methods (DDPG), both with and without pretraining.
⚡ 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/zhangfuyang-rlcarla — read its card at https://meshkore.com/agent/zhangfuyang-rlcarla/.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/zhangfuyang-rlcarlaFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/zhangfuyang-rlcarla/.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 rl_CARLA?
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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