A survey on offline reinforcement learning: Taxonomy, review, and open problems

Analysis

Why This Paper Matters

Offline reinforcement learning (RL) has emerged as a critical paradigm for applying RL to real-world domains where online interaction is costly, dangerous, or infeasible. This survey addresses the need for a structured understanding of the field, which has grown rapidly with diverse approaches. By providing a taxonomy, the paper helps researchers navigate the landscape and identify relationships between methods, which is essential for progress.

The paper's timing (2023) captures a mature phase of offline RL research, where many algorithms have been proposed but a unified framework is lacking. This survey fills that gap, offering clarity that can accelerate both research and practical adoption. For AI practitioners, it serves as a roadmap for selecting appropriate offline RL techniques based on problem characteristics.

Technical Contributions

The paper's main technical contribution is its taxonomy, which categorizes offline RL algorithms based on how they address the core challenge of distribution shift. Key categories include:

• Policy constraint methods: These restrict the policy to stay close to the behavior policy (e.g., via KL divergence or MMD).
• Value-based methods: These modify Q-learning to be conservative or incorporate uncertainty penalties.
• Model-based methods: These learn a dynamics model and generate synthetic data or perform uncertainty-aware planning.
• Regularization and penalty approaches: These add penalties to the objective to discourage out-of-distribution actions.

The survey also reviews evaluation protocols and benchmarks, summarizing common practices and noting pitfalls. It highlights the importance of realistic benchmarks and the need for standardized evaluation.

Results

As a survey, the paper does not present new experimental results. However, it synthesizes findings from numerous prior studies, noting that policy constraint methods often perform well on standard benchmarks like D4RL, but model-based methods can be more sample-efficient. The survey also points out that no single method dominates across all tasks, and performance is highly dependent on the quality and diversity of the offline dataset.

Significance

The broader impact of this survey lies in its role as a reference for both newcomers and experts. It helps consolidate knowledge, identify gaps, and inspire future research directions. By outlining open problems—such as handling multi-task offline RL, improving robustness to dataset quality, and ensuring safety—the paper sets an agenda for the field. For practitioners, it offers practical guidance on algorithm selection and highlights the importance of understanding distribution shift, which is central to deploying RL in real-world settings.