Language Assistance in Reinforcement Learning in Dynamic Environments

Language is an intuitive and effective way for humans to communicate. Large Language Models (LLMs) can interpret and respond well to language. However, their use in deep reinforcement learning is limited as they are sample inefficient. State-of-the-art deep reinforcement learning algorithms are more sample efficient but cannot understand...

Replacing the acquisition function in Bayesian optimization by a neural network: How effectively do meta-learned acquisition functions in Bayesian optimization perform when optimizing for control variates of unknown functions, as compared to BO with standard acquisition functions

Bayesian Optimization (BO) has demonstrated significant utility across numerous applications. However, due to it being designed as a universal optimizer, its performance can often be suboptimal in specialized environments. To overcome this issue, research has been conducted into the application of transfer learning for enhancing BO performance...