Path planning algorithms for steerable catheters, must guarantee anatomical obstacles avoidance, reduce the insertion length and ensure the compliance with needle kinematics. The majority of the solutions in literature focuses on graph based or sampling based methods, both limited by the impossibility to directly obtain smooth trajectories. In this work we formulate the path planning problem as a reinforcement learning problem and show that the trajectory planning model, generated from the training, can provide the user with optimal trajectories in terms of obstacle clearance and kinematic constraints. We obtain 2D and 3D environments from MRI images processing and we implement a GA3C algorithm to create a path planning model, able to generalize on different patients anatomies. The curvilinear trajectories obtained from the model in 2D and 3D environments are compared to the ones obtained by A* and RRT* algorithms. Our method achieves state-of-the-art performances in terms of obstacle avoidance, trajectory smoothness and computational time proving this algorithm as valid planning method for complex environments.