Stochastic Environment

Stochastic environments, characterized by unpredictable state transitions and outcomes, pose significant challenges for reinforcement learning (RL) and control systems. Current research focuses on developing robust algorithms and models, such as distributional RL, model-based methods incorporating uncertainty estimates, and hierarchical approaches, to address the limitations of traditional RL in these settings. This work is crucial for advancing the reliability and safety of AI agents in real-world applications, including autonomous driving, robotics, and resource management in dynamic systems, where uncertainty is inherent. The development of provably robust policies and efficient exploration strategies remains a key focus.