  In this paper we focus on developing a control algorithm for multi-terrain
tracked robots with flippers using a reinforcement learning (RL) approach. The
work is based on the deep deterministic policy gradient (DDPG) algorithm,
proven to be very successful in simple simulation environments. The algorithm
works in an end-to-end fashion in order to control the continuous position of
the flippers. This end-to-end approach makes it easy to apply the controller to
a wide array of circumstances, but the huge flexibility comes to the cost of an
increased difficulty of solution. The complexity of the task is enlarged even
more by the fact that real multi-terrain robots move in partially observable
environments. Notwithstanding these complications, being able to smoothly
control a multi-terrain robot can produce huge benefits in impaired people
daily lives or in search and rescue situations.
