Reinforcement Learning-based Threat Assessment
This addresses a domain-specific challenge in game scenarios for improving threat evaluation, but it is incremental as it applies existing reinforcement learning methods to this problem.
The paper tackles the problem of threat assessment in game scenarios with uncertain enemy units and attribute priorities by transforming it into a reinforcement learning problem, resulting in an efficient neural network evaluator that achieves more accurate and scientific threat assessment.
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.