Mastering the Limits: A New Way to Generalize Offline RL

Offline reinforcement learning (RL) faces a big problem: errors can pile up when deep Q functions try to work outside the data they were trained on. This hurts how well the policy can perform on new stuff. Existing methods are too cautious, which isn't great for generalization. But here's an interesting find: deep Q functions do really well inside the area covered by training data. This discovery led to a new method called DOGE. DOGE combines the shape of the data set with the deep function approximators in offline RL. Instead of sticking strictly to the data distribution, it allows the policy to explore new, generalizable areas outside of it. DOGE works by training a state-conditioned distance function. This function can be easily added to standard actor-critic methods as a policy constraint. The method is simple but powerful, showing better generalization than current top methods on D4RL benchmarks. Theory backs up DOGE's superiority over methods that only focus on data distribution or support constraints. It's a fresh take on how to handle out-of-distribution (OOD) areas in RL.