Imandra
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Imandra

PyIDF: Diversity of experiences in Reinforcement Learning

In Reinforcement Learning, an agent learns from interaction with its environment. The agent’s goal is to arrive at a behavior policy that maximizes the expected reward. Intuitively, an agent should learn from a diverse set of experiences, but how one can quantify and ensure this “diversity”? At Imandra, we’ve developed a novel technique for exploring algorithm state-spaces called Region

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