SEED RL: central accelerator inference with remote environments

What changed

Moved policy inference to centralized accelerators with streaming RPC communication while environments ran remotely, integrating existing V-trace and recurrent Q-learning algorithms.

Scientific scope

Moved policy inference to centralized accelerators with streaming RPC communication while environments ran remotely, integrating existing V-trace and recurrent Q-learning algorithms. Inherits IMPALA/V-trace and R2D2 rather than inventing their learning rules. Original2019report differs from2020conference date. Cost reductions40–80percent and throughput figures concern selected benchmarks and infrastructure assumptions, not current cloud prices or every environment/communication regime.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits IMPALA/V-trace and R2D2 rather than inventing their learning rules. Original2019report differs from2020conference date. Cost reductions40–80percent and throughput figures concern selected benchmarks and infrastructure assumptions, not current cloud prices or every environment/communication regime.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

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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