Pablo discusses the complexities of liquidity management for banks, highlighting two key problems: the initial liquidity decision akin to a bandit problem and the intraday payment decisions that involve sequential decision-making. He also touches on the intersection of graph theory and reinforcement learning, noting the challenges of multi-agent settings and the relevance of game theory concepts like Nash equilibria in finding optimal solutions.
