The following is the idea of a paper, please analyze the main arguments and main problems of the research. In addition, please propose a new thesis research idea and innovative idea based on the problem, and elaborate in detail.

A disadvantage of policy learning in offline reinforcement learning (RL) is the accumulation of errors in deep Q-functions in out-of-distribution (OOD) regions. Unfortunately, existing offline RL methods are often overly conservative, inevitably compromising generalization performance outside the data distribution.
An interesting observation in this paper's research is that the deep Q-function is well approximated within the convex hull of the training data. Inspired by this, the author proposes a new method DOGE (Distance-sensitive Offline RL with better GENeralization). DOGE combines dataset geometry with deep function approximators in offline RL, and develops in generalizable OOD regions, rather than strictly constraining policies in data distributions.
Specifically, DOGE trains a state-conditional distance function that can be easily plugged into standard actor-critic methods as policy constraints. Compared with the state-of-the-art methods on the D4RL benchmark, our algorithm is simple and elegant, and has better generalization ability. Theoretical analysis demonstrates that our method outperforms existing methods based only on data distribution or support constraints.
 Avoid using the following keywords: detail, rl, generalization, thesis
user