Non Stationary Reinforcement Learning

Non-stationary reinforcement learning (RL) addresses the challenge of training agents in environments where the underlying dynamics or reward functions change over time. Current research focuses on developing algorithms that can adapt to these changes efficiently, employing techniques like meta-learning, periodic policy updates, and causal-origin representations to manage uncertainty and improve robustness. This field is crucial for deploying RL in real-world applications where environments are inherently dynamic, impacting areas such as robotics, resource management, and personalized medicine.