SEED RL

Introduced by Espeholt et al. in SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

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).

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

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Reinforcement Learning (RL)	1	50.00%
Vision and Language Navigation	1	50.00%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

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Distributed Methods

Distributed Reinforcement Learning