This paper presents a control strategy for autonomous truck-trailer systems based on deep reinforcement learning (Deep RL). The deep deterministic policy gradient (DDPG) is used for training the controller using a reward function defined in terms of the desired final state of the system. A fuzzy-logic approach is employed to avoid the truck-trailer jackknife state. Simulation results show that the designed controller exhibits similar performance to state-of-the-art controllers such as the linear-fuzzy controller but with a much simpler design process.