How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
This addresses the problem of solving CGO problems with offline datasets for researchers in reinforcement learning, offering a novel approach but appearing incremental in method.
The paper tackles Contextual Goal-Oriented (CGO) problems by proposing CODA, a method that uses unlabeled trajectories and context-goal pairs to create a fully labeled transition dataset without approximation error, outperforming baseline methods across various relationships.
We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-Oriented (CGO) problems. By carefully constructing an action-augmented MDP that is equivalent to the original MDP, CODA creates a fully labeled transition dataset under training contexts without additional approximation error. We conduct a novel theoretical analysis to demonstrate CODA's capability to solve CGO problems in the offline data setup. Empirical results also showcase the effectiveness of CODA, which outperforms other baseline methods across various context-goal relationships of CGO problem. This approach offers a promising direction to solving CGO problems using offline datasets.