Introduction to GTrXL
GTrXL is a new architecture for reinforcement learning based on the popular transformer model. This architecture introduces a few key architectural modifications to improve the stability and learning speed of the original transformer and XL variant.
Key Modifications of GTrXL
A few key modifications are introduced in GTrXL to improve its performance. One of the modifications is the placement of layer normalization on only the input stream of the submodules. This change allows an identity map from the input of the transformer at the first layer to the output of the transformer after the last layer.
Another critical modification in GTrXL is the replacement of residual connections with gating layers. The researchers found that GRUs (Gated Recurrent Units) were the most effective form of gating in their experiments. These modifications help to improve the stability and learning speed of the model.
Why Use GTrXL?
GTrXL is designed specifically for reinforcement learning tasks, and its modifications improve the learning speed and stability of the model. Reinforcement learning is a field of machine learning in which an agent learns to interact with its environment to achieve a goal. The agent receives feedback in the form of rewards or penalties, and it learns to make better decisions to maximize its rewards. GTrXL is well-suited to this task because of its architecture and modifications.
In summary, GTrXL is a new architecture based on the popular transformer model designed specifically for reinforcement learning tasks. The modifications to this architecture improve the stability and learning speed of the model. These modifications include placing the layer normalization on only the input stream of the submodules and replacing residual connections with gating layers. By using GTrXL, agents can learn to make better decisions in their environment, achieving their goals more efficiently and effectively.
