Fresher Experience Plays a More Important Role in Prioritized Experience Replay

Fresher Experience Plays a More Important Role in Prioritized Experience Replay

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to measure the value of experiences and adjusts the...

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Bibliographic Details
Main Authors: Jue Ma, Dejun Ning, Chengyi Zhang, Shipeng Liu
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Applied Sciences
Subjects:
reinforcement learning
prioritized experience replay
experience freshness
deep Q-networks
deep deterministic policy gradient
Online Access:https://www.mdpi.com/2076-3417/12/23/12489

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https://www.mdpi.com/2076-3417/12/23/12489

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