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Deep Reinforcement Learning Task Assignment Based on Domain Knowledge
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DOI：10.1109/ACCESS.2022.3217654.png)
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Abstract
En 中文
Deep Reinforcement Learning (DRL) methods are inefficient in the initial strategy exploration process due to the huge state space and action space in large-scale complex scenarios. This is becoming one of the bottlenecks in their application to large-scale game adversarial scenarios. This paper proposes a Safe reinforcement learning combined with Imitation learning for Task Assignment (SITA) method for a representative red-blue game confrontation scenario. Aiming at the problem of difficult sampling of Imitation Learning (IL), this paper combines human knowledge with adversarial rules to build a knowledge rule base; We propose the Imitation Learning with the Decoupled Network (ILDN) pre-training method to solve the problem of excessive initial invalid exploration; In order to reduce invalid exploration and improve the stability in the later stages of training, we incorporate Safe Reinforcement Learning (Safe RL) method after pre-training. Finally, we verified in the digital battlefield that the SITA method has higher training efficiency and strong generalization ability in large-scale complex scenarios.
Keywords:
Deep learning
Games
Reinforcement learning
Sensors
Task analysis
Behavioral sciences
Reinforcement learning
Knowledge engineering
Deep reinforcement learning
imitation learning
knowledge rule base
safe reinforcement learning
task assignment