Robot Learning · Reward Design · M5-L3 · Intermediate
Balance Reward Terms
Experiment log · Scout at Meridian General Hospital
Experiment log, Meridian General Hospital. Facilities have asked for three things from Scout at once: get to the pharmacy quickly, never fall down the stairwell, and stop weaving between the supply carts like a shopping trolley. Each wish became a term in the reward: a cost per step, a penalty for stepping onto the stairwell edge, and a penalty for every change of heading. Nobody said how large each term should be.
You will not write any learning code in this lab. You set the weights; the page solves the resulting decision problem exactly with value iteration, then measures what Scout does with it: expected steps, the probability of a fall and the expected number of turns. A trade-off curve shows how those quantities move as one weight is swept, and a final stage asks for one setting that meets all three limits on wards you have not seen.
Your task
Lab complete: post-mortem
You have passed every stage. Now connect it back to the theory and move on:
Output
What Python can I use?
Plain Python with math and heapq. Your code runs in a background worker: an endless loop is stopped after a few seconds and the page stays usable. It runs in your browser and is never sent anywhere.