Also, web-links to several textbooks that have appeared on this subject in recent times have been provided. You will also find information related to miscellaneous items such as tutorials,software and demos. A brief description of Reinforcement Learning Reinforcement Learning is a simulation-based technique for solving Markov Decision Problems. Classical dynamic programming algorithms such as value iteration and policy iteration can be used to solve these problems if their state-space is small and the system under study is not very complex. However if the system under study is very complex or if the number of states is very large, these classical algorithms break down.
Reinforcement learning is a simulation-based method rooted in dynamic programming.
It is capable of solving large-scale markov decision problems. It does not require the computation or storage of the transition probabilites. When the state-space is large, it can be combined with a function approximation scheme such as regression or a neural network algorithm to approximate the value function of dynamic programming, thereby generating a solution. It has been shown through mathematically rigorous arguments that reinforcement learning can produce optimal or near-optimal solutions. There is also a great deal of empirical evidence to show the same.