IMPALA
IMPALA (Importance Weighted Actor-Learner Architecture) is a large-scale distributed reinforcement learning framework proposed by DeepMind. Building on A3C, it introduces a V-trace correction mechanism to address the off-policy lag problem, compensating for behavioral differences between the collection policy and the learning policy and supporting larger-scale parallel training.
Timeline (last 90 days)
Architectures such as IMPALA and Ape-X are representative implementations of the sampling-training decoupling pattern, achieving throughput improvements of tens of times compared to single-machine synchronous training on tasks such as Atari.
Actor-Learner decoupling is a common approach in distributed reinforcement learning, and architectures such as A3C, IMPALA, and Ape-X are all based on this concept.
IMPALA introduces a V-trace correction mechanism to handle the off-policy lag problem between the collection policy and the learning policy.
All Facts (3)
Architectures such as IMPALA and Ape-X are representative implementations of the sampling-training decoupling pattern, achieving throughput improvements of tens of times compared to single-machine synchronous training on tasks such as Atari.
50%UnverifiedActor-Learner decoupling is a common approach in distributed reinforcement learning, and architectures such as A3C, IMPALA, and Ape-X are all based on this concept.
50%UnverifiedIMPALA introduces a V-trace correction mechanism to handle the off-policy lag problem between the collection policy and the learning policy.
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