Design and edit MDP terms and wire them into configs and logging.
npx skills add https://github.com/yihangchen1205/mdp-designer-skill --skill mdp-designer
Please help me install this Agent Skill. Skill: mdp-designer Source: https://github.com/yihangchen1205/mdp-designer-skill/tree/main Command: npx skills add https://github.com/yihangchen1205/mdp-designer-skill --skill mdp-designer
Designing and editing MDP terms (observations, rewards, terminations, goals/commands, and randomization) and wiring them into configs and logging to improve an RL environment's learning signal.
Run a smoke test by adjusting the MDP term boundary (e.g., observations or rewards) in your env and executing a short rollout to verify behavior.
High-intent search queries and answers about installing and using this skill.
To tune MDP terms for better RL learning, you map targeted changes to the corresponding module boundaries such as observations, rewards, and terminations. This approach uses a config-driven method to apply minimal updates that preserve backwards compatibility while improving the environment's learning signal.
A config-driven approach for editing RL environments involves modifying MDP terms like observations, rewards, and randomization directly through configuration files. It enables targeted tweaks and clear logging without requiring extensive code changes, ensuring minimal, verifiable updates that preserve backwards compatibility.
You validate MDP changes produce stable learning signals by running a sanity-check plan that includes smoke runs and unit checks. This process verifies that your config-driven adjustments to observations or rewards execute correctly during a short rollout.
Yes, you can use this MDP design approach with Gymnasium-style environments. It applies across simulator-backed RL environments and custom training loops that have a well-defined observation, reward, and termination interface, allowing you to wire MDP term changes directly into your configs.
The best way to adjust term boundaries in a reinforcement learning environment is to identify the specific MDP module boundaries first, then apply minimal, config-driven changes. This targeted strategy maintains backwards compatibility while enabling clear logging for your environment's learning signal.
Preserving backwards compatibility when modifying termination and goal commands ensures that existing training loops and simulator-backed RL environments do not break. It allows you to apply targeted config-driven tweaks and verify them with a smoke run without disrupting previously stable learning signals.
projectedanxCreate agentic RL environments with multi-turn tool-calling loops and reward verification.
karaabd23-cryptoAutomate end-to-end Parallels smoke tests across macOS, Windows, and Linux guests.
Ritabrata-ChakrabortyTrain single-agent reinforcement learning models in Gymnasium environments with PyTorch.
thinking-machines-labConfigures and runs GRPO reinforcement learning workflows with the Tinker API for math, code, and custom environments.
