Safe Exploration Techniques and Reward Shaping Strategies in Reinforcement Learning for High-Risk Environments
Mohammad Faiz Afzal, Deepa Jananakumar · 2025
This book chapter explores advanced techniques in safe exploration and reward shaping for reinforcement learning (RL) in high-risk environments. The focus was on developing strategies that balance safety and task efficiency, ensuring that RL agents can explore uncertain environments without incurring catastrophic failures. Emphasis was placed on risk-aware exploration algorithms, which mitigate unsafe actions by evaluating the inherent risks during decision-making. Additionally, the chapter investigates dynamic reward shaping to adapt rewards in real-time, reinforcing safe exploration while maintaining task performance. By combining these methods, RL agents can navigate complex domains such as autonomous systems, healthcare, and finance, where both safety and efficiency are paramount. A thorough evaluation framework for safety performance and task completion efficiency was also presented, offering practical insights into optimizing RL agents for real-world applications. This chapter provides a comprehensive foundation for future research in safe and effective RL applications in high-stakes scenarios.