  We introduce a stochastic learning process called the dampened gradient
approximation process. While learning models have almost exclusively focused on
finite games, in this paper we design a learning process for games with
continuous action sets. It is payoff-based and thus requires from players no
sophistication and no knowledge of the game. We show that despite such limited
information, players will converge to Nash in large classes of games. In
particular, convergence to a Nash equilibrium which is stable is guaranteed in
all games with strategic complements as well as in concave games; convergence
to Nash often occurs in all locally ordinal potential games; convergence to a
stable Nash occurs with positive probability in all games with isolated
equilibria.
