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Acme: reusable actor-learner components across execution scales

Contribution grade

Reinforcement-learning research infrastructure · Human-designed research method or resource

Reference score120,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Separated RL actors, learners, replay and environment interfaces into composable components that support shared agent code in single-process and distributed execution.

Scientific scope

Separated RL actors, learners, replay and environment interfaces into composable components that support shared agent code in single-process and distributed execution. Implements inherited RL algorithms; modularity does not prove independent reproduction of every result or exact policy-performance invariance at all scales. Data/learning rate and execution configuration matter.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Implements inherited RL algorithms; modularity does not prove independent reproduction of every result or exact policy-performance invariance at all scales. Data/learning rate and execution configuration matter.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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