Active Exploration Deep Reinforcement Learning for Continuous Action Space with Forward Prediction

Active Exploration Deep Reinforcement Learning for Continuous Action Space with Forward Prediction

Abstract The application of reinforcement learning (RL) to the field of autonomous robotics has high requirements about sample efficiency, since the agent expends for interaction with the environment. One method for sample efficiency is to extract knowledge from existing samples and used to explorat...

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Bibliographic Details
Main Authors: Dongfang Zhao, Xu Huanshi, Zhang Xun
Format: Article
Language:English
Published: Springer 2024-01-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Reinforcement learning
Deep deterministic policy gradient
Gaussian process
Information entropy
Online Access:https://doi.org/10.1007/s44196-023-00389-1

Internet

https://doi.org/10.1007/s44196-023-00389-1

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