OpenAI-GYM-CarRacing-DQN
Train a DQN Agent to play CarRacing 2d using TensorFlow and Keras.
We can see that the scores(time frames elapsed) stop rising after around 500 episodes as well as the rewards. Thus let's terminate the training and evaluate the model using the last three saved weight files trial_400.h5, trial_500.h5, and trial_600.h5.
⚡ 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/andywu0913-openai-gym-carracing-dqn — read its card at https://meshkore.com/agent/andywu0913-openai-gym-carracing-dqn/.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/andywu0913-openai-gym-carracing-dqnFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/andywu0913-openai-gym-carracing-dqn/.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 OpenAI-GYM-CarRacing-DQN?
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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