Beyond State-Wise Mirror Descent: Offline Policy Optimization with Parameteric Policies
arXiv:2602.23811v1 Announce Type: new Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., Xie et al., 2021) have established the theoretical foundations of learning a good policy from offline data via pessimism, existing algorithms that are computationally tractable (often in an oracle-efficient sense), such as PSPI, only apply to finite and small action spaces. Moreover, these algorithms rely on state-wise mirror descent and require actors to be implicitly induced from the critic functions, failing to accommodate standalone policy parameterization which is ubiquitous in practice. In this work, we address these limitations and extend the theoretical guarantees to parameterized policy classes over large or continuous action spaces. When extending mirror descent to parameterized policies, we identify contextual coupling as the core difficulty, and show how connecting mirror descent to natu
arXiv:2602.23811v1 Announce Type: new Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., Xie et al., 2021) have established the theoretical foundations of learning a good policy from offline data via pessimism, existing algorithms that are computationally tractable (often in an oracle-efficient sense), such as PSPI, only apply to finite and small action spaces. Moreover, these algorithms rely on state-wise mirror descent and require actors to be implicitly induced from the critic functions, failing to accommodate standalone policy parameterization which is ubiquitous in practice. In this work, we address these limitations and extend the theoretical guarantees to parameterized policy classes over large or continuous action spaces. When extending mirror descent to parameterized policies, we identify contextual coupling as the core difficulty, and show how connecting mirror descent to natural policy gradient leads to novel analyses, guarantees, and algorithmic insights, including a surprising unification between offline RL and imitation learning.
Executive Summary
This article explores the theoretical aspects of offline reinforcement learning under general function approximation, extending existing algorithms to accommodate parameterized policy classes over large or continuous action spaces. The authors address limitations of prior works, such as state-wise mirror descent, and propose a novel approach that unifies offline RL and imitation learning. The article provides new analyses, guarantees, and algorithmic insights, enabling the application of offline RL to more complex and realistic scenarios.
Key Points
- ▸ Extension of offline RL to parameterized policy classes
- ▸ Addressing limitations of state-wise mirror descent
- ▸ Unification of offline RL and imitation learning
Merits
Theoretical Rigor
The article provides a thorough and rigorous analysis of the theoretical aspects of offline RL, establishing a solid foundation for future research.
Algorithmic Innovations
The proposed approach offers novel algorithmic insights and guarantees, enabling the application of offline RL to more complex scenarios.
Demerits
Computational Complexity
The article may not fully address the computational complexity of the proposed approach, which could be a limitation in practice.
Expert Commentary
The article makes a significant contribution to the field of offline RL, addressing key limitations of prior works and proposing a novel approach that unifies offline RL and imitation learning. The authors' use of contextual coupling and natural policy gradient leads to new analyses and guarantees, which have important implications for the development of more effective and efficient policies. However, further research is needed to fully address the computational complexity of the proposed approach and to explore its applications in practice.
Recommendations
- ✓ Further research on the computational complexity of the proposed approach
- ✓ Exploration of the article's implications for imitation learning and function approximation