We present a framework to solve for best responses and equilibria in extensive-form games (EFGs) of imperfect information by transforming a game together with an other-agent policy into a set of Markov decision processes (MDPs), one per player, and then
In unsupervised learning, there is no output related to the inputs, meaning that the data is unlabeled. Unsupervised learning must find the existing patterns and relationships between data samples. In reinforcement learning (RL), the agent and the dynamic environment work in relation to each other...