Learning the Heater From Its Own Logs

说明与用途

作者用 4,800 条记录训练了新的加热器模型，取代了手工编写的模型，获得了更准确的温度预测。

问题

A hand‑written world‑model for a heater used arbitrary constants and was inaccurate for a real room. The model needed to learn the true dynamics from logged data.

结果

The trained model outperformed the hand‑coded model, predicting room temperatures more accurately on unseen data.

工作流

• Define realRoom function with true constants and noise.
• Generate 80 episodes of 60 steps each, recording state, action, and nextState.
• Use a thermostat to decide actions, injecting random actions every fifth step.
• Collect the log of Transition objects.
• Train a new predictive model on the logged data.
• Evaluate the trained model against unseen room states.
• Run the complete system in the browser.

证据

The model is four numbers. The training loop is twenty lines.

来源

Eight episodes gives 4,800 transitions, each one the Transition type from the 101 post.

来源

The rule for the rest of the post is that nothing below this line gets to look inside realRoom.

来源