The DDPG described in that paper is very good to start working in control problems. There are many implementations of this algorithm online. However, bare in mind though that the algorithm is very unstable and needs many hours of monitoring and tuning the parameters of the networks. I would suggest you to create a very simple scenario (simulator) and put the DDPG to interact with it. It is very important to understand what your networks are learning and which state features are important or no. Furthermore the reward function will play a very important role in the performance of your algorithm.
Another important point is the way you formalize your problem will determine if you are trying to solve a one-step decision making problem (e.g. given state features ---> credit limit value, reward) or a sequential decision making problem (e.g. given state features ---> credit limit value, reward, new state features and so on). This will specify if you need to modify the Q learning part of the Critic and remove the $maxQ(s',a')$ part (one step decision case).
Regarding pretraining, as Neil suggested you can use regression to pretrain Actor and Critic networks so the weights are initialized to better than random values. A very interesting approach from Deepmind is the one that is described in this paper Learning from Demonstrations for real-world RL, and most likely helped them optimize the energy consumption at their server centers.