Bayesian Deep Reinforcement Learning via Deep Kernel Learning

Junyu Xuan, Jie Lu, Zheng Yan, Guangquan Zhang

Abstract: Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems could benefit from RL, e.g., industrial robotics, medical treatment, and trade execution. As a representative...