Deep Learning
国際会議
Auxiliary selection: optimal selection of auxiliary tasks using deep reinforcement learning
- Author
- Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
- Publication
- The 8th IIEEJ International Conference on Image Electronics and Visual Computing, 2024
A method using auxiliary task is a type of multi-task learning. This improves the performance of the target task by simultaneously learning auxiliary task. However, this method requires that the auxiliary task must be effective for the target task. It is very difficult to determine in advance whether a designed auxiliary task is effective, and the effective auxiliary task changes dynamically according to the learning status of the target task. Therefore, we propose an auxiliary task selection mechanism, Auxiliary Selection, based on deep reinforcement learning. We confirmed the effectiveness of our method by introducing it to UNREAL, a method that has achieved high agent performance by introducing auxiliary tasks.