Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers

With transformers, While transformers have gained the reputation as the this information can be effectively captured and modeled, "Swiss army knife of AI", no one has challenged them whereas with CNNs it can be more challenging. This is one to master the game of chess, one of the classical AI reason, why nowadays transformer models are taken over benchmarks. Simply using vision transformers (ViTs) classical CNN approaches in computer vision and other domains within AlphaZero does not master the game of chess, [6]. Moreover, by combining the strengths of transformers mainly because ViTs are too slow. Even making them more and reinforcement learning (RL), it is possible to efficient using a combination of MobileNet and NextViT develop powerful models for solving complex sequential does not beat what actually matters: a simple change of the decision-making problems [2, 13]. They can be used to input representation and value loss, resulting in a greater model the state representation, policy, and value function, boost of up to 180 Elo points over AlphaZero.