One major problem of current state-of-the-art Reinforcement Learning (RL) algorithms is still the need for millions of training examples to learn a good or near-optimal policy to solve the given task. This plays especially a critical role for real-world applications in the industry be it for robotics or other complex optimization problems for decision making or optimal control.
Due to these problems, engineers and researchers are looking for ways to improve this sample-inefficiency to increase the speed of learning and the need for gathering millions of expensive training examples.

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