Reinforcement Learning-- An Introduction to Gradient Temporal Difference Learning Algorithms

Reinforcement learning is one of the hottest fields to be in right now, with concrete applications growing at an incredibly rapid pace, from beating video games to robotics. At its essence, reinforcement learning (RL) deals with decision making --i.e. it attempts to answer the question of how an agent should act in a given environment. Loosely speaking, all of RL comes down to either finding or evaluating a policy, which is just a way of behaving. For example, a policy could be a playing strategy in chess. A policy takes a state -- in the chess example, the position of all the pieces on the board -- and assigns an action to it. For example, given the state of your chess board, your policy might ask you to move your queen forward.