The goal is to find the global optimal arm, and agents are able to pull any arm; however, they can only observe the reward when the selected arm is local.

While training with static demonstrations has shown some promise, we show that such methods fall short for controlling real GUIs due to their failure to deal with real world stochasticity and non-stationarity not captured in static observational data.

Vision-and-language navigation in the real-world is an important step towards building mobile agents that perceivetheir environments and complete specific tasks following human instructions.

However, directly applying existing subgame solving techniques may be difficult, due to the intricate nature and substantial size of many real-world games.

Modeling the behavior of intelligent agents is an essential subject for autonomous systems. Safe operations of autonomous agents require accurate prediction of other agents' future motions.

Methods for sequential decision making are often built upon a foundational assumption that the underlying decision process is stationary [Sutton and Barto, 2018]. While this assumption was a cornerstone when laying the theoretical foundations of the field, and while is often reasonable, it isseldom trueinpractice andcanbeunreasonable [Dulac-Arnold etal.,2019].