Addressing Data Scarcity in Reinforcement Learning: Inference and Decision-Making under Complex Environments
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This project addresses the increasing demand for reliable and interpretable reinforcement learning (RL) systems that can function effectively in complex, data-limited environments. RL has demonstrated potential in fields such as healthcare and public policy, where decisions often need to be made with limited and noisy data. However, ensuring that…
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