Novel Deep Reinforcement Learning Algorithm Based on Attention-based Value Function and Autoregressive Environment Model
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TP311

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National Natural Science Foundation of China (71701205)

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    Abstract:

    Recently, deep reinforcement learning (DRL) is believed to be promising in continuous decision-making and intelligent scheduling problems, and some examples such as AlphaGo, OpenAI Five, and Alpha Star have demonstrated the great generalization capability of the paradigm. However, the inefficient utility of collected experience dataset in DRL restricts the universal extension to more practical scenarios and complicated tasks. As the auxiliary, the model-based reinforcement learning can well capture the dynamics of environment and bring the reduction in experience sampling. This study aggregates the model-based and model-free reinforcement learning algorithms to formulate an end-to-end framework, where the autoregressive environment model is constructed, and attention layer is incorporated to forecast state value function. Experiments on classical CartPole-V0 and so on witness the effectiveness of proposed framework in simulating environment and advancing utility of dataset. Finally, penetration mission as the practical instantiation is successfully completed with the framework.

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梁星星,冯旸赫,黄金才,王琦,马扬,刘忠.基于自回归预测模型的深度注意力强化学习方法.软件学报,2020,31(4):948-966

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History
  • Received:May 31,2019
  • Revised:July 29,2019
  • Adopted:
  • Online: January 14,2020
  • Published: April 06,2020
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