Construct · 1 fields · 1 citations

proximal policy optimization: a standalone construct

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Reinforcement learning (machine learning)

a policy gradient method that clips the policy update ratio so each step stays close to the previous policy, giving stable training.

In reinforcement learning (machine learning), proximal policy optimization refers to a policy gradient method that clips the policy update ratio so each step stays close to the previous policy, giving stable training. This meaning comes from Schulman (2017) in arXiv.[1]

Unit
Recovery means
Measured by

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Nearby constructs

gradient4 fields · 4 citationsjingle · one word, four constructs learning6 fields · 14 citationsjingle · one name, six constructs policy gradient1 fields · 1 citationsanchor · a standalone construct

References

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv. https://doi.org/10.48550/arxiv.1707.06347

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