Methods › Reinforcement Learning › Distributed Reinforcement Learning › SEED RL
SEED RL
Introduced by Lasse Espeholt et al. in SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SEED (Scalable, Efficient, Deep-RL) is a scalable reinforcement learning agent. It utilizes an architecture that features centralized inference and an optimized communication layer. SEED adopts two state of the art distributed algorithms, IMPALA/V-trace (policy gradients) and R2D2 (Q-learning).
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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VALAN: Vision and Language Agent Navigation 6 Dec 2019 · 1 repository · arXiv:1912.03241
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SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference 15 Oct 2019 · 2 repositories · arXiv:1910.06591Syntology ran 0 of 6 samples · 6 unverified
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Reinforcement Learning | 2 |
| Deep Reinforcement Learning | 1 |
| Q-Learning | 1 |
| Reinforcement Learning (RL) | 1 |
| Vision and Language Navigation | 1 |
| reinforcement-learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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