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cs.LG2024
Towards characterizing the value of edge embeddings in Graph Neural Networks
Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton +3
Graph neural networks (GNNs) are the dominant approach to solving machine learning problems defined over graphs. Despite much theoretical and empirical work in recent years, our un…
cs.LG2024
The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation
Noah Golowich, Ankur Moitra
In this paper, we study the offline RL problem with linear function approximation. Our main structural assumption is that the MDP has low inherent Bellman error, which stipulates t…
cs.LG2024
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
Noah Golowich, Ankur Moitra
One of the most natural approaches to reinforcement learning (RL) with function approximation is value iteration, which inductively generates approximations to the optimal value fu…