Introduction

In the recent advancement of the machine learning field, we start to discuss reinforcement learning more and more. Reinforcement learning differs from supervised learning, where we should be very familiar with, in which they do not need the examples or labels to be presented. The focus of reinforcement learning is finding the right balance between exploration (new environment) and exploitation (use of existing knowledge).

Conceptual diagram of reinforcement learning

The environment of reinforcement learning generally describes in the form of the Markov decision process (MDP). Therefore, it would be a good idea for us to understand various Markov concepts; Markov chain, Markov process, and hidden Markov model (HMM).

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