Reinforcement Learning Using Bayesian Algorithms with Applications

Reinforcement learning (RL) has indeed become capable of performing a variety of complicated decision-making tasks, which was formerly inaccessible to machines. This type of learning is a trial-and-error training whereby an agent begins engaging with the surrounding by selecting control strategies based upon its uncontrolled (randomized) principle. Whenever the agent's activities result in the mission being completed successfully, the agent is rewarded. RL develops a model to create connections between the surrounding conditions, the activities that can be performed, and the benefits of the outcome. This chapter discusses the approach of the combination of RL with Bayesian algorithms. We discuss an in-depth analysis of how this concept is applied in different contexts with cases. Finally, we suggest some real-world applications using this combinational approach.