What Does a ProcGen Generalization Gap Measure? Action Rules, Residual Entropy, and the Missing Random Floor

Abhisek Keshari

A generalization gap in reinforcement learning, return on training levels minus return on held-out levels, is usually reported without a reference point. We argue that it should be read against a measured random floor: the return of a uniform-random policy on the same levels under the same evaluation harness. On eight ProcGen environments with PPO at a compute-limited budget (8M steps, 16 parallel environments; three games extended to 25M), the floor changes what standard numbers mean. The test-time action rule decides which policy is measured: in miner, the sampled policy scores 5.1x the floor on held-out levels while its argmax scores below it in every run, and greedy evaluation places two environments significantly below the floor. Used as a convergence diagnostic, raw policy entropy flags six of eight environments, but 32-66% of that entropy lies on actions with identical effects; against the floor, five of eight sampled policies are clearly above it on held-out levels and heist's is not distinguishable from it. An audit of twelve ProcGen codebases finds that nine sample test-time actions with no explicit choice at the evaluation call site. We recommend that every reported…