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Fall 2018
Temporal-difference (TD) learning is an important approach for predictive knowledge representation and sequential decision making. Within TD learning exists multi-step methods which unify one-step TD learning and Monte Carlo methods in a way where intermediate algorithms can outperform either...
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Fall 2024
Value-based reinforcement learning is an approach to sequential decision making in which decisions are informed by learned, long-horizon predictions of future reward. This dissertation aims to understand issues that value-based methods face and develop algorithmic ideas to address these issues....