Construct · 1 fields · 1 citations

asynchronous advantage actor-critic: a standalone construct

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Reinforcement learning (machine learning)

deep reinforcement learning in which many parallel actor-learners update a shared actor-critic network asynchronously, stabilizing training without a replay buffer.

In reinforcement learning (machine learning), asynchronous advantage actor-critic refers to deep reinforcement learning in which many parallel actor-learners update a shared actor-critic network asynchronously, stabilizing training without a replay buffer. This meaning comes from Mnih (2016) in arXiv.[1]

Unit
Recovery means
Measured by

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Nearby constructs

actor-critic1 fields · 1 citationsanchor · a standalone construct buffer3 fields · 3 citationsjingle · one word, three constructs experience replay1 fields · 1 citationsanchor · a standalone construct

References

Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016). Asynchronous Methods for Deep Reinforcement Learning. arXiv. https://doi.org/10.48550/arxiv.1602.01783

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