Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.02612 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908337116807168 |
|---|---|
| author | Sun, Wuzhou Li, Siyi Zou, Qingxiang Liao, Zixing |
| author_facet | Sun, Wuzhou Li, Siyi Zou, Qingxiang Liao, Zixing |
| contents | In some game scenarios, due to the uncertainty of the number of enemy units and the priority of various attributes, the evaluation of the threat level of enemy units as well as the screening has been a challenging research topic, and the core difficulty lies in how to reasonably set the priority of different attributes in order to achieve quantitative evaluation of the threat. In this paper, we innovatively transform the problem of threat assessment into a reinforcement learning problem, and through systematic reinforcement learning training, we successfully construct an efficient neural network evaluator. The evaluator can not only comprehensively integrate the multidimensional attribute features of the enemy, but also effectively combine our state information, thus realizing a more accurate and scientific threat assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02612 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning-based Threat Assessment Sun, Wuzhou Li, Siyi Zou, Qingxiang Liao, Zixing Machine Learning Artificial Intelligence In some game scenarios, due to the uncertainty of the number of enemy units and the priority of various attributes, the evaluation of the threat level of enemy units as well as the screening has been a challenging research topic, and the core difficulty lies in how to reasonably set the priority of different attributes in order to achieve quantitative evaluation of the threat. In this paper, we innovatively transform the problem of threat assessment into a reinforcement learning problem, and through systematic reinforcement learning training, we successfully construct an efficient neural network evaluator. The evaluator can not only comprehensively integrate the multidimensional attribute features of the enemy, but also effectively combine our state information, thus realizing a more accurate and scientific threat assessment. |
| title | Reinforcement Learning-based Threat Assessment |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.02612 |