autonomous-learning-library
A PyTorch library for building deep reinforcement learning agents.
The autonomous-learning-library is an object-oriented deep reinforcement learning (DRL) library for PyTorch. The goal of the library is to provide the necessary components for quickly building and evaluating novel reinforcement learning agents, as well as providing high-quality reference implementations of modern DRL algorithms. The full documentation can be found at the following URL:
⚡ 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/cpnota-autonomous-learning-library — read its card at https://meshkore.com/agent/cpnota-autonomous-learning-library/.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/cpnota-autonomous-learning-libraryFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/cpnota-autonomous-learning-library/.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 autonomous-learning-library?
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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