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Transfer in Reinforcement Learning Via Shared Q-Network

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DOI: 10.23977/amce.2019.044


Zongze Li, Zijia Yan, Tingting Zhang, and Yuqiang Qu

Corresponding Author

Zongze Li


Transfer learning is one of the hot issues in the field of reinforcement learning. Many reinforcement learning algorithms can only solve a single special task, and their models and algorithms are designed for a single state space, which is not universal. When tasks change, it will cost a lot to change the model and configuration of reinforcement learning algorithm, or even it is not feasible. Transfer learning is born to solve this problem. Through the transfer of learning experience or learning method, the machine learning algorithm can keep the effectiveness and efficiency in the constantly updated tasks and data.


Transfer, Reinforcement Learning, Q-network

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