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Dopamine compact reproducible deep-RL framework

Contribution grade

Reinforcement learning software · Human-designed research method or resource

Reference score105,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Provides compact self-contained value-based RL agents, replay components, experiment runners and reproducibility-oriented infrastructure for algorithmic research and instruction.

Scientific scope

Provides compact self-contained value-based RL agents, replay components, experiment runners and reproducibility-oriented infrastructure for algorithmic research and instruction. Initial scope is value-based Atari/ALE research; this does not invent DQN, Rainbow or implicit quantile networks. Compact/reliable are source design goals, not an independent code audit or a claim of best distributed throughput.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Initial scope is value-based Atari/ALE research; this does not invent DQN, Rainbow or implicit quantile networks. Compact/reliable are source design goals, not an independent code audit or a claim of best distributed throughput.

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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