Reinforcement learning has gained considerable traction as it mines real experiences with the help of trial-and-error learning to model decision-making. Thus, this approach attempts to imitate the fundamental method used by humans of learning optimal behavior without the requirement of an explicit model of the environment. In contrast to many other approaches from the domain of machine learning, reinforcement learning works well with learning tasks of arbitrary length and can be used to learn complex strategies for many scenarios, such as robotics and game playing.
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