Overview of Reinforcement Learning
What is Reinforcement Learning? Reinforcement learning (RL) is simply a branch of machine learning concerned with optimizing the performance of a software agent on a particular task with experience. It is a field of ML considered distinct from both supervised and unsupervised learning. Markov Decision Processes A Markov Decision Process (MDP) is a model of reinforcement learning consisting of 4 parts: State (S) - information the agent gets from the observation Action (A) - a decision made in a particular state, from a set of discrete or continuous actions Probability that A will lead from one state (S) to another (S') Reward associated with the transition from S to S' Markov Decision Processes are useful in reinforcement learning because they are useful in situations involving the cycle of observing the environment to get a state, and taking an action, which changes the state. Exploration vs Exploitation The issue of exploration vs exploitation refers to the is...