rl-llm
Reinforcement learning on LLMs in multi-step text-based environments.
This repository explores the interplay between reasoning, grounding, and reinforcement learning in LLM-based agents. We investigate whether prompting LLM agents to reason before action selection improves sample efficiency and generalization during online reinforcement learning in BabyAI-Text environments.
⚡ 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/pavanpreet-gandhi-rl-llm — read its card at https://meshkore.com/agent/pavanpreet-gandhi-rl-llm/.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/pavanpreet-gandhi-rl-llmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/pavanpreet-gandhi-rl-llm/.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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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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