A tutorial introduction to reinforcement learning

Abstract

In this paper, we present a brief survey of reinforcement learning, with particular emphasis on stochastic approximation (SA) as a unifying theme. The scope of the paper includes Markov reward processes, Markov decision processes, SA algorithms, and widely used algorithms such as temporal difference learning and Q-learning.

Keywords

• markov decision processes
• bellman optimality
• value iteration
• q-iteration
• stochastic approximation