Transformer Based World Model

Transformer-based world models aim to create efficient and accurate representations of environments for reinforcement learning agents, enabling more sample-efficient policy learning through imagined experiences. Current research focuses on improving the efficiency and long-term memory capabilities of these models, exploring architectures like Transformers, and variations incorporating stochasticity and hierarchical structures to handle complex dynamics and multi-agent scenarios. These advancements are significant because they enhance the sample efficiency and performance of reinforcement learning agents, potentially leading to more robust and adaptable AI systems across various applications.