behavior tree augmentation with reinforcement learning for enhanced npc behavior in games

In the video game industry, enhancing the realism and intelligence of character behavior is a central focus, with a growing interest in techniques that enable game characters to behave in more human-like ways. behavior trees are a widely adopted approach for implementing decision-making processes in game characters, facilitating responses to various in-game scenarios. however, even with well-designed behavior trees, intended outcomes are not always achieved due to suboptimal structuring and prioritization of actions. this study proposes a reinforcement learning-based method to enhance behavior trees, enabling more accurate and adaptive character reactions through automated behavior prioritization. specifically, a q-learning algorithm is applied to evaluate and reorder behaviors within the tree according to their effectiveness in achieving game objectives. to test this approach, we implemented the method in the computer game mario. using the epsilon-greedy strategy to guide action valuation, each behavior in the original tree was adjusted to optimize toward the game’s objectives. results show that mario’s performance improved by 17% compared to the baseline behavior tree, demonstrating the efficacy of q-learning in refining in-game character decision-making.