In their standard formulations, stochastic games and Markov decision processes assume a rational opponent or a stationary environment. Online learning algorithms can adapt to arbitrary opponents and non-stationary environments, but do not incorporate the dynamic structure of stochastic games or Markov decision processes. We survey recent approaches that apply online learning to dynamic environm...