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Reinforcement learning (RL) is one of the fundamental areas of machine learning and is widely regarded as a necessary component of artificial general intelligence (AGI). While RL has been researched for a few decades, the advent of deep learning has resulted in the so-called deep reinforcement learning algorithms that utilize deep neural networks and large-scale computing power to significantly improve the capabilities of RL. They have resulted in computer programs that play video games and board games at super human level and even beat handcrafted computer programs, e.g., chess program AlphaZero beat the best handcrafted program Stockfish 8. Besides video and board games, RL is being applied to enable robots to learn complex tasks in autonomous driving and industrial automation. This chapter covers the fundamental concepts of RL and the taxonomy of RL algorithms. In the next chapter, you will learn some of the key algorithms in reinforcement learning. An understanding of this chapter will provide you with a good feel for RL.
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- Components of Reinforcement Learning
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