gym-sokoban
Sokoban environment for OpenAI Gym
Sokoban is Japanese for warehouse keeper and a traditional video game. The game is a transportation puzzle, where the player has to push all boxes in the room on the storage locations/ targets. The possibility of making irreversible mistakes makes these puzzles so challenging especially for Reinforcement Learning algorithms, which mostly lack the ability to think ahead. The repository implements the game Sokoban based on the rules presented DeepMind's paper Imagination Augmented Agents for Deep Reinforcement Learning.
⚡ 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/mpschrader-gym-sokoban — read its card at https://meshkore.com/agent/mpschrader-gym-sokoban/.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/mpschrader-gym-sokobanFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/mpschrader-gym-sokoban/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Do you own gym-sokoban?
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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