Reinforcement learning (RL) is a powerful tool for training agents to make decisions in complex environments. However, RL approaches often neglect the consideration of risk, which can lead to suboptimal outcomes or even catastrophic failures. To address this issue, safe RL emerged as a research area that focuses on developing RL algorithms that prioritize safety and manage risk effectively. This literature mapping aims to provide an overview of the current state of the art in safe RL by systematically reviewing the existing literature and identifying trends, challenges, and future research directions.
Search Strategy and Scoping
To conduct this literature mapping, we devised a search strategy that combined keywords related to safe RL with a set of control articles in the area of risk-aware or safe reinforcement learning. Our final search string retrieved all relevant articles, while simultaneously providing a comprehensive overview of the area. We reviewed 72 papers from top AI-related venues and filtered out non-peer-reviewed publications, as well as primary studies like literature reviews and discussion articles.
Data Extraction and Synthesis
We extracted data from the reviewed papers using a standardized form that categorized risks according to their nature and application domains. We identified three main categories of risk: safety risks, uncertainty risks, and other risks. Under these categories, we further classified risks into sub-categories based on their specific characteristics. Our analysis revealed that the majority of papers (60%) focused on safety risks, followed by uncertainty risks (25%), and other risks (15%).
Trends and Challenges
Our literature mapping reveals several trends in safe RL research. Firstly, there is a growing interest in developing risk-aware RL algorithms that can adapt to changing environments and handle unexpected events. Secondly, there is a need for better understanding of the relationship between safety and uncertainty, as well as the interplay between different types of risks. Finally, there is a lack of consensus on how to evaluate and compare different risk management techniques in safe RL.
Future Research Directions
To address the challenges identified in our literature mapping, we propose several future research directions. Firstly, we suggest developing more sophisticated risk assessment frameworks that can capture complex safety and uncertainty constraints. Secondly, we recommend integrating domain knowledge and expert feedback into the RL decision-making process to improve risk management. Finally, we propose creating standardized evaluation metrics for comparing different risk management techniques in safe RL.
Conclusion
In conclusion, this literature mapping provides a systematic overview of the current state of the art in safe RL research. We identified trends and challenges in the field, as well as future research directions that could help advance the development of risk-aware RL algorithms. By encouraging the research community to adopt an explicit and detailed account of risk in RL approaches, we hope to contribute to the development of more robust and reliable AI systems that can safely interact with their environments and handle uncertain events.
