DQN or Deep-Q Networks were first proposed by DeepMind back in 2015 in an attempt to bring the advantages of deep learning to reinforcement learning(RL), Reinforcement learning focuses on training agents to take any action at a particular stage in an environment to maximise rewards. Reinforcement learning then tries to train the model to improve itself and its choices by observing rewards through interactions with the environment. A simple demonstration of such learning is seen in the figure below.
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