  In many real-world games, such as traders repeatedly bargaining with
customers, it is very hard for a single AI trader to make good deals with
various customers in a few turns, since customers may adopt different
strategies even the strategies they choose are quite simple. In this paper, we
model this problem as fast adaptive learning in the finitely repeated games. We
believe that past game history plays a vital role in such a learning procedure,
and therefore we propose a novel framework (named, F3) to fuse the past and
current game history with an Opponent Action Estimator (OAE) module that uses
past game history to estimate the opponent's future behaviors. The experiments
show that the agent trained by F3 can quickly defeat opponents who adopt
unknown new strategies. The F3 trained agent obtains more rewards in a fixed
number of turns than the agents that are trained by deep reinforcement
learning. Further studies show that the OAE module in F3 contains
meta-knowledge that can even be transferred across different games.
