Robust and Adaptive Online Decision Making
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Reinforcement learning is typically built upon the assumption that the environment is uncorrupted and fixed. This assumption does not hold anymore when there exists adversarial corruption or non-stationary transition. Many standard reinforcement learning algorithms are vulnerable to these factors – e.g., a tiny amount of corruption may totally alter the behavior of the algorithm. In the first part of the talk, I will present robust algorithms which achieve the optimal performance under corruption, and reduction techniques that turn a standard algorithm which only works for stationary environments into one that is robust to non-stationarity. The reductions are black-box, general, and optimal for a wide range of problems.
In the second part, I will focus on decentralized multi-agent reinforcement learning. Decentralized algorithms are easy to implement, versatile for different types of games, and scalable to systems with many agents, but they often suffer from non-convergence issues. We will discuss algorithmic techniques that facilitate the convergence of the system, while not introducing extra coordination or communication overhead.