Do We Need Transformers To Play FPS Video Games?

Abstract

In this paper, we explore the Transformer based architectures for reinforcement learning in both online and offline settings within the Doom game environment. Our investigation focuses on two primary approaches: Deep Transformer Q- learning Networks (DTQN) for online learning and Decision Transformers (DT) for offline reinforcement learning. DTQN leverages the sequential modelling capabilities of Transformers to enhance Q-learning in partially observable environments,while Decision Transformers repurpose sequence modelling techniques to enable offline agents to learn from past trajectories without direct interaction with the environment. We conclude that while Transformers might have performed well in Atari games, more traditional methods perform better than Transformer based method in both the settings in the VizDoom environment.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keybatth2025do

Related papers

• Transformer Based Reinforcement Learning For Games (2019)0.00
• Transforming Game Play: A Comparative Study Of DCQN And DTQN Architectures In Reinforcement Learning (2024)0.00
• When Should We Prefer Decision Transformers For Offline Reinforcement Learning? (2023)0.00
• Deep Reinforcement Learning For Doom Using Unsupervised Auxiliary Tasks (2018)0.00
• Q-learning Decision Transformer: Leveraging Dynamic Programming For Conditional Sequence Modelling In Offline RL (2022)0.00
• Vizdoom Competitions: Playing Doom From Pixels (2018)13.79
• Q-value Regularized Decision Convformer For Offline Reinforcement Learning (2024)0.00
• DODT: Enhanced Online Decision Transformer Learning Through Dreamer's Actor-critic Trajectory Forecasting (2024)0.00