Monte-Carlo method and artificial intelligence: application of Monte-Carlo method in reinforcement learning

Reinforcement learning is the fastest growing technology used in the creation of artificial intelligent systems. At the moment, this field is quite extensive. Many researchers around the world are actively working with reinforcement learning in various fields: neuroscience, control theory, psychology and many others. The purpose of this paper is to substantiate the possibility of using the Monte Carlo method in reinforcement learning. It is known that the main thing in such learning is to record aspects of a real problem when a learner interacts with the surrounding world to achieve his goal. That is, a learning agent must have a goal associated with the state of the environment. It is also necessary to be able to sense the environment and perform actions that affect it. The formulation of a reinforcement learning problem should take into account all three aspects - sensation, action and goal - in their simplest forms. Monte Carlo methods are able to solve reinforcement learning problems based on averaging sample results. In order to ensure the availability of clearly defined results, this article considers Monte Carlo methods only for episodic problems. Thus, Monte Carlo methods can be incremental at the episode level.