Best of Both World Algorithm

Best-of-Both-Worlds (BoBW) algorithms aim to design online learning methods that perform optimally in both stochastic and adversarial environments without prior knowledge of the environment type. Current research focuses on extending BoBW capabilities to various settings, including constrained Markov decision processes (CMDPs), bandits with delayed or partial feedback, and linear contextual bandits, often employing algorithms like Follow-the-Perturbed-Leader (FTPL) and Follow-the-Regularized-Leader (FTRL) with adaptive learning rates. These advancements offer significant improvements in robustness and efficiency for online learning systems, impacting fields like reinforcement learning and online advertising where unpredictable environments are common.