A Human-Machine Agent Based on Active Reinforcement Learning for Target Classification in Wargame
ABSTARCT : To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to autonomously classify detected targets into predefined categories with equipment information.
To simulate different levels of human guidance, we set up two modes.
The easier-to-obtain but low-value-type cues simulated by Mode 1 and the labor-intensive but high-value class labels simulated by Mode 2.
This technique involves an agent (in this case, the machine agent) learning to make decisions (such as target classification) by interacting with its environment (the wargame scenario).
The agent receives feedback in the form of rewards or penalties based on its actions, which helps it improve its decision-making abilities over time.
This implies that human operators and machine agents work together synergistically.
The human might provide high-level strategic guidance or domain expertise, while the machine agent handles tasks like target classification autonomously.
Developing and maintaining a reinforcement learning (RL) system can be complex and resource-intensive.
It requires expertise in both machine learning and the specific domain (wargaming in this case).
RL algorithms often require large amounts of training data and computational resources to achieve optimal performance.
In the context of wargames, obtaining sufficient and diverse training data can be challenging due to the sensitivity and complexity of real-world scenarios.
This aspect emphasizes the involvement of human decision-makers in the loop.
While the agent learns from data and feedback, human expertise and judgment are crucial in guiding the learning process, setting objectives, and interpreting results.
In wargames, identifying and classifying targets accurately and quickly is critical for making informed decisions.
The system aims to improve this capability by leveraging both machine learning algorithms and human input.
The agent can continuously learn and adapt its classification strategies based on feedback received during gameplay or simulations.
This adaptive learning capability improves its effectiveness over time.
Active reinforcement learning allows the agent to prioritize and select which targets to classify, optimizing the use of computational resources and human attention.
Once trained, the system can potentially scale to handle large volumes of data and complex scenarios, providing consistent and reliable performance in various wargaming environments.
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