Deep Reinforcement Learning That Matters.¶
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018). Deep Reinforcement Learning That Matters. Proceedings of the AAAI Conference on Artificial Intelligence, 3207-3214.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Minimal Pairs
- Phonological pairs leak prosody; A/B tests leak time-of-day and traffic source; ablations leak training noise and seed variance; lesion studies leak compensatory reorganization.
This sourceDocuments how random-seed variance and non-determinism confound reported gains, and prescribes averaging over seeds / pre-declaring held-constant factors — the leakage failure of ablation-style comparisons and its defense.
- Phonological pairs leak prosody; A/B tests leak time-of-day and traffic source; ablations leak training noise and seed variance; lesion studies leak compensatory reorganization.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:57b9cbcd027c · see in the full table