Satisfied, Not Solving: Wireheading of Bounded Homeostatic Rewards in Reinforcement Learning

Abstract:Bounded intrinsic rewards are often assumed to be safe from exploitation: if a signal can yield at most a fixed amount, gaming it should not pay. We test this for a homeostatic satiation reward, in which needs build up and relieving them is rewarding, and give the agent a cheap action that resets its needs. In MiniGrid DoorKey-8x8, PPO agents with this action solved 2 of 80 seeds, against 71 of 80 on the same seeds without it; in KeyCorridorS3R1, 0 of 20 against 20 of 20. Their policies stayed near uniform. The reward does not ask for this: a telescoping argument caps any policy's satiation return at the needs' starting drive, and a post-hoc computation shows that, at every growth rate we trained, a solving trajectory beats that cap on all sampled layouts. In DoorKey with advantage normalization on, a satisfaction signal the agent could fill itself was worse than none (11 of 20 solved with task reward alone, 1 of 20 with the tamperable signal), and a per-press cost of 0.1 restored the task in every seed. Farming of positive reward cycles appeared in DoorKey but not persistently in KeyCorridorS3R1. All predictions were pre-registered, and hash-locked from round 3 on; 9 of our 23 hypotheses were not supported, and we report each.

33 pages, 5 figures, 11 tables. Code and data: this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2611.01183 [cs.LG]

(or arXiv:2611.01183v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2611.01183 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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