A Hierarchical Hybrid AI Approach: Integrating Deep Reinforcement Learning and Scripted Agents in Combat Simulations
📝 Abstract
In the domain of combat simulations in support of wargaming, the development of intelligent agents has predominantly been characterized by rule-based, scripted methodologies with deep reinforcement learning (RL) approaches only recently being introduced. While scripted agents offer predictability and consistency in controlled environments, they fall short in dynamic, complex scenarios due to their inherent inflexibility. Conversely, RL agents excel in adaptability and learning, offering potential improvements in handling unforeseen situations, but suffer from significant challenges such as black-box decision-making processes and scalability issues in larger simulation environments. This paper introduces a novel hierarchical hybrid artificial intelligence (AI) approach that synergizes the reliability and predictability of scripted agents with the dynamic, adaptive learning capabilities of RL. By structuring the AI system hierarchically, the proposed approach aims to utilize scripted agents for routine, tactical-level decisions and RL agents for higher-level, strategic decision-making, thus addressing the limitations of each method while leveraging their individual strengths. This integration is shown to significantly improve overall performance, providing a robust, adaptable, and effective solution for developing and training intelligent agents in complex simulation environments.
💡 Analysis
In the domain of combat simulations in support of wargaming, the development of intelligent agents has predominantly been characterized by rule-based, scripted methodologies with deep reinforcement learning (RL) approaches only recently being introduced. While scripted agents offer predictability and consistency in controlled environments, they fall short in dynamic, complex scenarios due to their inherent inflexibility. Conversely, RL agents excel in adaptability and learning, offering potential improvements in handling unforeseen situations, but suffer from significant challenges such as black-box decision-making processes and scalability issues in larger simulation environments. This paper introduces a novel hierarchical hybrid artificial intelligence (AI) approach that synergizes the reliability and predictability of scripted agents with the dynamic, adaptive learning capabilities of RL. By structuring the AI system hierarchically, the proposed approach aims to utilize scripted agents for routine, tactical-level decisions and RL agents for higher-level, strategic decision-making, thus addressing the limitations of each method while leveraging their individual strengths. This integration is shown to significantly improve overall performance, providing a robust, adaptable, and effective solution for developing and training intelligent agents in complex simulation environments.
📄 Content
In the domain of combat simulations in support of wargaming, the development of intelligent agents has predominantly been characterized by rule-based, scripted methodologies with deep reinforcement learning (RL) approaches only recently being introduced. Scripted methodologies-a term we use in this paper to generally refer to strategies governed by predefined sets of rules and behaviors-have been instrumental in creating effective, predictable, and logical agents for most environments. However, their rigidity and inability to adapt to unforeseen scenarios or circumstances have typically limited their effectiveness-ultimately leading to predictable outcomes, suboptimal performance, and diminished value when used for wargaming or operational planning. RL, on the other hand, provides a framework for agents to learn and adapt through direct interactions with their environment, allowing agents to improve their behaviors over time, learn from past experiences, generalize from these experiences, and adapt to changing conditions within the simulation environment. Nevertheless, the application of RL in large combat simulations is not without its challenges, primarily due to the complexity of these environments and the inefficiencies associated with learning in large state spaces. Additionally, the black-box nature of RL models can make the decision-making process opaque, making it difficult to trust and interpret the actions taken by RL agents. This paper proposes a hierarchical hybrid artificial intelligence (AI) approach that integrates RL and scripted agents in combat simulations to improve performance beyond either approach alone. By structuring the AI system hierarchically, we aim to leverage the strengths of both methods while mitigating their respective weaknesses. Scripted agents are employed to handle well-defined, routine tasks and to provide a consistent baseline behavior at the tactical level, while RL agents are utilized for longer-term decision-making and adaptation in response to evolving circumstances at the operational or strategic level. This hybrid approach aims to create a more robust and effective AI system overall. Specifically, our investigation explores ways to optimize training efficacy given the constraint of limited computational budgets-a typical challenge in the practical application of RL to combat simulations.
Traditional decision-making algorithms such as rule-based systems, behavior trees (BTs), goal-based systems, and finite state machines (FSMs) are examples of approaches central to agent design in games and simulations (Millington, 2006). Most strategy games and combat simulations implementing intelligent agents employ these types of hard-coded methodologies due to their reliability, predictability, and ease of implementation. In this paper, we refer to these types of approaches that do not rely on machine learning as Scripted Agents.
Rule-based systems, grounded in if-then logic, offer a methodical framework for agent decision-making. They provide a structured approach, allowing for decisions to be made based on specific, predefined criteria, which results in clear and consistent actions. BTs extend this structure, organizing decisions in a hierarchical manner that mirrors natural decision-making processes, thereby enhancing system flexibility while providing a distinct delineation of decision pathways. This structure not only facilitates easier updates and modifications but also supports varied behavioral patterns. FSMs simplify the representation of agent states, breaking down behaviors into clear, discrete stages with defined transitions, thus facilitating targeted problem-solving. Incorporating goal-based systems into this framework allows agents to pursue specific objectives, allowing them to align their actions and strategies toward achieving these goals. These methodologies together create a strong base for crafting agents that perform reliably and consistently-driven by well-defined rules and logical frameworks-thus allowing for reasonable decisionmaking across most situations. However, while this design based on specific domain knowledge can lead to effective and predictable levels of performance in familiar situations, relying on predefined rules, heuristics, and algorithms often comes with inherent inflexibility and rigidity, making them less effective in unexpected or novel situations (Kwasny & Faisal, 1990). The scripted agent’s reliance on fixed logic and predetermined pathways often limits the agent’s ability to adapt to larger, more dynamic environments (Colledanchise & Ögren, 2018;Millington, 2006)-underscoring the need for more advanced, adaptable AI approaches capable of learning and responding to novel challenges, while still leveraging the consistency and predictability of scripted methodologies.
Reinforcement learning is a subset of machine learning that involves an agent learning to make decisions through direct interaction with its environment. A reinfo
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