Time Varying Environment

Time-varying environments, where system dynamics or reward structures change over time, pose significant challenges for decision-making algorithms. Current research focuses on developing robust algorithms, such as Thompson Sampling variants and model-based approaches like those employing linear function approximation within contextual Markov Decision Processes (CMDPs), to address this non-stationarity. These efforts aim to minimize regret—the difference between optimal and achieved performance—in various settings, including abruptly and smoothly changing environments, and even those impacted by the agent's own actions (performative reinforcement learning). Improved understanding of these methods holds significant implications for autonomous systems, resource management, and other applications requiring adaptive control in dynamic contexts.