Revolutionizing NPC Interaction: DQN with Huber Loss for Dynamic Behavior Modeling in Video Games

This research aims to improve the interaction between non-player characters (NPCs) and players in video games. It proposes a new method that combines Deep Q- Networks (DQN) with Huber loss. Current strategies for simulating non-player character (NPC) behavior frequently encounter difficulties in successfully managing the trade-off between exploration and exploitation, leading to less than optimum user experiences. The solution we propose utilizes the advantages of DQN and Huber loss to overcome these restrictions. By utilizing Huber loss, our model attains resilience to anomalies and more gradual gradients, hence improving learning efficacy and steadiness. Moreover, the use of DQN enables flexible decision-making, resulting in more authentic NPC behavior. We have conducted thorough experiments to prove that our methodology is better to existing approaches. The average reward achieved by our system is 85, and it maintains a balanced ratio of 70% for exploration and 30% for exploitation. The learning curve reaches a point of convergence, suggesting consistent learning, while the value of the loss function lowers by 60%, exhibiting enhanced error reduction during the training process. The results highlight the efficacy of our suggested strategy in transforming NPC interaction in video games.