Non-Stationary RL

Non-stationary reinforcement learning studies how agents can keep making good decisions when rewards, transitions, or task conditions change, as they do in deployed robots, traffic systems, and markets. Approaches include change-point detection and regret-based algorithms, context and meta-learning, replay-data management, and continual-learning methods such as network resets or successor features. Theoretical work gives guarantees in tabular and linear Markov decision processes, while empirical evaluations span robotics, driving, games, and multi-agent settings. A central tension is how to adapt quickly without forgetting useful past knowledge or violating safety constraints, especially when old conditions may return.