Reinforcement Learning: Monte Carlo Method

An easy-to-understand explanation of the Monte-Carlo method for Reinforcement learning

In Reinforcement learning, the learner or the decision maker, called the Agent, constantly interacts with its Environment by performing actions sequentially at each discrete time step. Interaction of the Agent with its Environment changes the Environment's state, and as a result, the Agent receives a numerical reward from the Environment.