  A technique for speeding up reinforcement learning algorithms by using time
manipulation is proposed. It is applicable to failure-avoidance control
problems running in a computer simulation. Turning the time of the simulation
backwards on failure events is shown to speed up the learning by 260% and
improve the state space exploration by 12% on the cart-pole balancing task,
compared to the conventional Q-learning and Actor-Critic algorithms.
