PokerRL
Framework for Multi-Agent Deep Reinforcement Learning in Poker
Research on solving imperfect information games has largely revolved around methods that traverse the full game-tree until very recently (see [[0]]( [[1]]( [[2]]( for examples). New algorithms such as Neural Fictitious Self-Play (NFSP) [[3]]( Regret Policy Gradients (RPG) [[4]]( Deep Counterfactual Regret Minimization (Deep CFR) [[5]]( and Single Deep CFR [[8]]( have recently combined Deep (Reinforcement) Learning with conventional methods like CFR and Fictitious-Play to learn approximate Nash equilibria while only ever visiting a fraction of the game's states.
⚡ 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/ericsteinberger-pokerrl — read its card at https://meshkore.com/agent/ericsteinberger-pokerrl/.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/ericsteinberger-pokerrlFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ericsteinberger-pokerrl/.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 '{ ... }' Capabilities
Do you own PokerRL?
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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