Title: High Dimensional Bayesian Optimization with Reinforced Transformer Deep Kernels. Abstract: Bayesian Optimization (BO) has proved to be an invaluable technique for efficient, high-dimensional optimization. The use of Gaussian process (GP) surrogates and dynamic acquisition functions has allowed BO to shine in challenging high dimensional optimization due to its sample efficiency and uncertainty modeling. Reinforcement Learning has been introduced to improve optimization performance on both single function optimization as well as \textit{few-shot} multi-objective optimization. However, until now, even few-shot techniques treat each objective as independent optimization tasks, failing to exploit the similarities shared between objectives. We combine recent developments in Deep Kernel Learning (DKL) and attention-based Transformer models to improve the modeling powers of GP surrogates with meta-learning. We propose a method for improving meta-learning BO surrogates by incorporating attention mechanisms into DKL, empowering the surrogates to adapt to contextual information gathered during the BO process. This Transformer Deep Kernel is combined with Reinforcement Learning techniques to aid in exploration, ensuring state-of-the-art results on a variety of high-dimensional optimization problems.