AWhat changed
A reinforcement-learning result is easier to interpret when the task, observations and reward have a consistent definition. The 2018 DeepMind Control Suite supplies a collection of continuous-control tasks, Python interfaces and baseline evaluations built on MuJoCo. It separates physical domains from task objectives and uses a common reward scale for most benchmark tasks. This makes it easier to compare learning curves and inspect failure cases. The contribution here is the original benchmark resource and interface design, not the invention of MuJoCo or the algorithms evaluated with it.
Scientific scope
A reinforcement-learning result is easier to interpret when the task, observations and reward have a consistent definition. The 2018 DeepMind Control Suite supplies a collection of continuous-control tasks, Python interfaces and baseline evaluations built on MuJoCo. It separates physical domains from task objectives and uses a common reward scale for most benchmark tasks. This makes it easier to compare learning curves and inspect failure cases. The contribution here is the original benchmark resource and interface design, not the invention of MuJoCo or the algorithms evaluated with it. Most default observations are state-derived features, with pixel observations available separately. Fixed 1,000-step returns are evaluation proxies, not proofs about infinite-horizon performance. Baselines use different numbers of seeds and parallel actors; their settings are starting points, not upper bounds for each algorithm. Stability checks are empirical. The later 2020 dm_control account adds software and tasks with a different byline; those additions are not silently included in this 2018 award.
Evidence
The linked original works support the scoped statement and contribution attribution. WRS has not independently replayed experiments or reconstructed a full mathematical proof. Most default observations are state-derived features, with pixel observations available separately. Fixed 1,000-step returns are evaluation proxies, not proofs about infinite-horizon performance. Baselines use different numbers of seeds and parallel actors; their settings are starting points, not upper bounds for each algorithm. Stability checks are empirical. The later 2020 dm_control account adds software and tasks with a different byline; those additions are not silently included in this 2018 award.
Contributors
- The named authors in the research record · SCIENCE: Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.
Provisional WRS assessment, edition 2026-10-23.6. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
Full scientific record · Browse contribution grades · Request a correction