  A mechanism called Eligibility Propagation is proposed to speed up the Time
Hopping technique used for faster Reinforcement Learning in simulations.
Eligibility Propagation provides for Time Hopping similar abilities to what
eligibility traces provide for conventional Reinforcement Learning. It
propagates values from one state to all of its temporal predecessors using a
state transitions graph. Experiments on a simulated biped crawling robot
confirm that Eligibility Propagation accelerates the learning process more than
3 times.
