MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Control Theory

Background:

• Model-based offline reinforcement learning (RL) offers sample efficiency and generalizability.
• Existing methods often use restricted policy spaces, limiting their potential.
• There's a need for practical, model-based offline RL with unrestricted policies.

Purpose of the Study:

• To develop a model-based offline RL algorithm, MoMA, that utilizes general function approximations and an unrestricted policy class.
• To address the limitations of existing approaches in leveraging the full advantages of model-based methods.
• To provide theoretical guarantees and a practical implementation for the proposed algorithm.

Main Methods:

• Developed MoMA, a model-based mirror ascent algorithm for offline RL.
• Employed general function approximations for policy updates, moving beyond restricted parametric classes.
• Incorporated conservative value function estimation within a confidence set of transition models.
• Established theoretical guarantees on the suboptimality bound of the returned policy.

Main Results:

• MoMA effectively utilizes an unrestricted policy class in model-based offline RL.
• The algorithm demonstrates improved decision-making capabilities compared to existing methods.
• Theoretical analysis provides an upper bound on the suboptimality of MoMA's policy.
• A practical, approximate version of MoMA was developed and validated.

Conclusions:

• MoMA represents a significant advancement in model-based offline RL by enabling unrestricted policies.
• The algorithm offers a practical and theoretically sound approach for complex decision-making problems.
• Numerical studies confirm the effectiveness and potential of MoMA in real-world applications.