A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems (2203.01387v3)

Abstract: With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment. Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets. Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics. In this work, we contribute with a unifying taxonomy to classify offline RL methods. Furthermore, we provide a comprehensive review of the latest algorithmic breakthroughs in the field using a unified notation as well as a review of existing benchmarks' properties and shortcomings. Additionally, we provide a figure that summarizes the performance of each method and class of methods on different dataset properties, equipping researchers with the tools to decide which type of algorithm is best suited for the problem at hand and identify which classes of algorithms look the most promising. Finally, we provide our perspective on open problems and propose future research directions for this rapidly growing field.

• The paper introduces a taxonomy of offline RL methods that categorizes algorithms into model-based, trajectory, and model-free strategies.
• The paper reviews key algorithmic advancements such as BCQ, BEAR, and implicit Q-learning that address distributional shifts and sparse rewards.
• The paper identifies open challenges including robust hyperparameter tuning, unsupervised RL techniques, and safety-critical policy optimization for future research.

Insights into Offline Reinforcement Learning: Taxonomy, Review, and Open Problems

This paper provides a comprehensive survey on offline reinforcement learning (RL), a branch of RL that allows an agent to learn policies from static datasets without further interaction with the environment. Offline RL presents a significant potential for real-world applications where online data collection is either infeasible or risky, such as healthcare, education, and autonomous driving. The research conducted by Prudencio, Maximo, and Colombini elucidates the taxonomy of offline RL methods while reviewing recent algorithmic advances and highlighting open challenges in the field.

Unifying Taxonomy of Offline RL Methods

The authors propose a novel taxonomy aimed at categorizing offline RL methods, allowing researchers to make informed choices about algorithm design. At a high level, these algorithms either adopt model-based approaches, learn from trajectory distributions, or leverage model-free strategies to directly learn policies from datasets. The taxonomy incorporates components such as model rollouts, planning for trajectory optimization, actor-critic methods (with choices between one-step and multistep), and imitation learning strategies.

Additionally, the authors delineate several optional modifications to offline RL algorithms, which include policy constraints, importance sampling, regularization, uncertainty estimation, and model-based strategies. These modifications are characterized by "has-a" relationships that may be employed to enhance the algorithm's performance. This detailed classification scheme allows researchers to identify promising methods or combinations of methods for specific offline RL applications.

Recent Algorithmic Developments

The review of recent algorithmic breakthroughs includes works such as BCQ, BEAR, BRAC, CQL, and more, each showcasing distinct approaches to addressing challenges endemic to offline RL. The authors highlight methods utilizing implicit and direct policy constraints, innovative regularization techniques to improve Q-function estimation, and various uncertainty measurement strategies aimed at enhancing conservative policy learning.

Recent one-step methods, such as implicit Q-learning (IQL), demonstrate efficacy in tackling distributional shifts through optimal dynamic programming strategies. Methods like Decision Transformer and Trajectory Transformer signify promising advancements in trajectory optimization, enabling the construction of sophisticated sequence models to better handle sparse-reward environments.

Benchmark Performance and Evaluation

In evaluating offline RL methods, the paper reviews existing benchmarks, notably D4RL and RL Unplugged, discussing their properties and limitations. The absence of benchmarks addressing stochastic dynamics, nonstationarity, and complex agent interactions is observed. Furthermore, the authors emphasize the necessity of reliable OPE methods for hyperparameter selection and early model validation.

The comparative performance analysis among recent algorithms finds trajectory optimization and one-step methods—augmented by implicit policy constraints and value regularization—as notably successful across various dataset properties. Emmons et al.'s RvS and Janner et al.'s TT illustrate the power of sequence modeling in environments requiring multitasking or sparse reward attribution.

Future Directions and Open Challenges

While significant strides have been made, the authors articulate several open research areas, such as robust hyperparameter tuning methods and the development of unsupervised RL techniques for harnessing unlabelled data. Incremental RL emerges as a promising area, particularly for managing nonstationary datasets. Addressing safety-critical RL remains a pertinent challenge, requiring the incorporation of risk-sensitive objectives within policy optimization frameworks.

The authors envisage compelling future developments where offline RL methodologies may further extend into domains demanding high-dimensional perception and decision-making, positing that leveraging diverse unlabeled datasets could prove transformative.

Conclusion

This survey addresses the intricacies of offline RL and proposes a systematic taxonomy to aid future research. The review of recent methods and benchmarks, combined with insights into open challenges, serves as a fundamental resource for researchers aiming to advance this field. Prudencio and colleagues delineate the theoretical and practical implications of offline RL, paving the path for innovations across myriad applications that were once beyond the reach of traditional RL paradigms.