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Chase J. Metoyer

Publications and source records attributed to Chase J. Metoyer.

2 recordsLinked to original sources

Found by Search, Broken by a Shortcut: Transfer and Tampering of Searched Intrinsic Rewards

Can a search over innate drives find intrinsic rewards that help a reinforcement-learning agent learn, transfer to a task it never saw, and resist an exploit that was absent during the search? Each candidate reward combines four generic events (a new view, a pickup, a drop and a toggle), each with a weight, a relief and a refill rate. We ran three pre-registered stages; the search protocol was locked in a public, timestamped commit before any search result existed. CMA-ES and an equal-budget random search used 1,560 PPO training runs, and the final evaluation and tamper test used 340. On the held-out task, KeyCorridorS3R2, the CMA-ES-selected reward (E) beat task-only PPO, episodic novelty and the random-search-selected reward (R) in all 3 registered comparisons, all of them meaningful by the registered size rule. Among these generic rewards E transferred best; the earlier paper's satiation reward, which on this task is given hand-wired key and locked-door events, did better (20 of 20 seeds solved against 4 of 20; descriptive). On UnlockPickup, where that reward uses generic events, E's mean area exceeded it by 0.276 (descriptive). On the training tasks there was no registered difference between E and R: both E-over-R tests missed. A free reset action, absent during the search, collapsed E's learning on DoorKey-8x8 (mean area 0.829 without it, 0.136 with it; registered) to below task-only PPO (descriptive). The searched drives helped on every task tested, but a reward given task-specific events did better on the held-out task, and selection gave E no protection against an exploit absent from the search.

cs.LG↗

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

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.

cs.LG↗