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Article summary:
1. A black-box reduction is proposed that turns a reinforcement learning algorithm with optimal regret in a stationary environment into another algorithm with optimal dynamic regret in a non-stationary environment, without any prior knowledge of the degree of non-stationarity.
2. The algorithm achieves the optimal dynamic regret for various scenarios such as multi-armed bandits, linear bandits, episodic MDPs, and infinite-horizon MDPs.
3. Previous works only obtain suboptimal bounds and/or require knowledge of the number and amount of changes of the world.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence to support its claims through examples and experiments. The authors have also provided detailed explanations for their approach which makes it easier to understand the concept. Furthermore, they have compared their results to previous works which shows that their approach is an improvement over existing methods.
However, there are some potential biases in the article that should be noted. For example, the authors do not explore counterarguments or present both sides equally when discussing their approach versus existing methods. Additionally, they do not mention any possible risks associated with their approach which could lead to an incomplete understanding of its implications. Finally, there is some promotional content in the article as it focuses mainly on highlighting the advantages of their approach rather than exploring its limitations or drawbacks.