The purpose of this tutorial is to provide an introduction to reinforcement learning (RL) at a level easily understood by students and researchers in a wide range of disciplines. The intent is not to present a rigorous mathematical discussion that requires a great deal of effort on the part of the reader, but rather to present a conceptual framework that might serve as an introduction to a more rigorous study of RL. The fundamental principles and techniques used to solve RL problems are presented. The most popular RL algorithms are presented. Section 1 presents an overview of RL and provides a simple example to develop intuition of the underlying dynamic programming mechanism. In Section 2 the parts of a reinforcement learning problem are discussed. These include the environment, reinforcement function, and value function. Section 3 gives a description of the most widely used reinforcement learning algorithms. These include TD(λ) and both the residual and direct forms of value iteration, Q-learning, and advantage learning. In Section 4 some of the ancillary issues in RL are briefly discussed, such as choosing an exploration strategy and an appropriate discount factor. The conclusion is given in Section 5. Finally, Section 6 is a glossary of commonly used terms followed by references in Section 7 and a bibliography of RL applications in Section 8. The tutorial structure is such that each section builds on the information provided in previous sections. It is assumed that the reader has some knowledge of learning algorithms that rely on gradient descent (such as the backpropagation of errors algorithm).
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