  This paper extends the notion of learning equilibrium in game theory from
matrix games to stochastic games. We introduce Foolproof Cooperative Learning
(FCL), an algorithm that converges to a Tit-for-Tat behavior. It allows
cooperative strategies when played against itself while being not exploitable
by selfish players. We prove that in repeated symmetric games, this algorithm
is a learning equilibrium. We illustrate the behavior of FCL on symmetric
matrix and grid games, and its robustness to selfish learners.
