Neural Circuit Policies for Virtual Character Control

Abstract

The development of high-stakes decision-making neural agents that interact with complex environments, such as video games, is an important aspect of AI research with numerous potential applications. Reinforcement learning combined with deep learning architectures (DRL) has shown remarkable success in various genres of games. The performance of DRL is heavily dependent upon the neural networks resides within them. Although these algorithms perform well in offline testing but the performance deteriorates in noisy and sub-optimal conditions, creating safety and security issues. To address these, we propose a hybrid deep learning architecture that combines a traditional convolutional neural network with worm brain-inspired neural circuit policies. This allows the agent to learn key coherent features from the environment and interpret its dynamics. The obtained DRL agent was not only able to achieve an optimal policy quickly, but it was also the most noise-resilient with the highest success rate. Our research indicates that only 20 control neurons (12 inter-neurons and 8 command neurons) are sufficient to achieve competitive results. We implemented and analyzed the agent in the popular video game Doom, demonstrating its effectiveness in practical applications.