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SEED RL: central accelerator inference with remote environments

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

What is recorded here?

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

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

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.

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People and systems

Lasse Espeholt; Raphaël Marinier; Piotr Stanczyk; Ke Wang; Marcin Michalski

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.

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English catalogue narrative

SEED RL: central accelerator inference with remote environments

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

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.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Lasse Espeholt; Raphaël Marinier; Piotr Stanczyk; Ke Wang; Marcin Michalski

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.

Original sources