• Corpus ID: 221139557

An adaptive synchronization approach for weights of deep reinforcement learning

@article{Badran2020AnAS,
  title={An adaptive synchronization approach for weights of deep reinforcement learning},
  author={Saeed Badran and Mansoor Rezghi},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.06973}
}
Deep Q-Networks (DQN) is one of the most well-known methods of deep reinforcement learning, which uses deep learning to approximate the action-value function. Solving numerous Deep reinforcement learning challenges such as moving targets problem and the correlation between samples are the main advantages of this model. Although there have been various extensions of DQN in recent years, they all use a similar method to DQN to overcome the problem of moving targets. Despite the advantages… 
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15 References

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