How to Solve Contextual Goal-Oriented Problems with Offline Datasets?

2024-08-14

Neural Information Processing Systems

AI Summary

This work addresses the Contextual Goal-Oriented (CGO) problem under the offline setting, proposing the first purely offline solution framework that requires no online interaction. To handle unlabeled trajectories and context-goal pairs, we introduce an action-augmented equivalent MDP, enabling generation of fully labeled training data with zero approximation error. We further establish, for the first time, a theoretical framework characterizing solvability and error bounds for CGO in the strictly offline regime. Our method integrates contextual goal modeling, offline reinforcement learning, and rigorous theoretical guarantees. Empirical evaluation across diverse context-goal relationship scenarios demonstrates significant improvements over existing baselines, validating the effectiveness, generalization capability, and robustness of our offline CGO approach.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Solving contextual goal-oriented problems with offline datasets
• Using unlabeled trajectories and context-goal pairs effectively
• Creating fully labeled transition datasets without approximation error

Innovation

• Uses unlabeled trajectories and context-goal pairs
• Constructs action-augmented MDP without approximation error
• Demonstrates effectiveness via theoretical and empirical analysis