Transforming Game Play: A Comparative Study of DCQN and DTQN Architectures in Reinforcement Learning

Abstract

In this study, we investigate the performance of Deep Q-Networks utilizing Convolutional Neural Networks (CNNs) and Transformer architectures across three different Atari games. The advent of DQNs has significantly advanced Reinforcement Learning, enabling agents to directly learn optimal policies from high-dimensional sensory inputs from pixel or RAM data. While CNN-based DQNs have been extensively studied and deployed in various domains, Transformer-based DQNs are relatively unexplored. Our research aims to fill this gap by benchmarking the performance of both DCQNs and DTQNs across the Atari games Asteroids, Space Invaders, and Centipede. We find that in the 35-40 million parameter range, the DCQN outperforms the DTQN in speed across both ViT and Projection Architectures. We also find the DCQN outperforms the DTQN in all games except for Centipede.