Reinforcement learning is a universal platform for learning optimal behavior policies in the environment under exploration. Policy optimality is achieved by maximizing the rewards received from the environment during interaction with it. But herein lies one of the main problems of this approach. The creation of an appropriate reward function often requires significant human effort. Additionally, rewards may be sparse and/or insufficient to express the true learning goal. As one of the options ...
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