  Stochastic processes with random reinforced relocations have been introduced
in the physics literature to model animal foraging behaviour. Such a process
evolves as a Markov process, except at random relocation times, when it chooses
a time at random in its whole past according to some ``memory kernel'', and
jumps to its value at that random time.
  We prove a quenched large deviations principle for the value of the process
at large times. The difficulty in proving this result comes from the fact that
the process is not Markov because of the relocations. Furthermore, the random
inter-relocation times act as a random environment.
