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Reinforcement Learning (RL) is a rapidly growing area of interest in the Artificial Intelligence community, with tremendous applications. As a result, there is a need to improve efficiency and exploration in RL algorithms to promote quicker and improved learning. We introduce MIRL: Mutual Information for Beneficial Exploration in RL, which considers the use of the mutual information between an action and the expected "future" from a given state as an additional reward to improve exploration. Using MIRL, agents learn to exploit "decision states" that lead to highly specialized futures.

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