Abstract: Forestry machines operated in forest production environments face challenges when performing manipulation tasks, especially regarding to the complicated dynamics of underactuated crane systems and the different sizes of logs to be grasped. This study investigates the feasibility of using reinforcement learning for forestry crane manipulators in grasping and balancing a varying-diameter wood log in a simulation environment. The Mujoco physics engine creates realistic scenarios, including modeling a forestry crane with $8$ degrees of freedom from CAD data and wood logs of different sizes. Our results show the successful implementation of a velocity controller for log grasping by deep reinforcement learning using a curriculum strategy. More specifically, given the six degrees of freedom (DoF) poses of the wood log, i.e., the 3D Cartesian position and the orientation, the control strategy exhibits a success rate of 96% when grasping logs of different diameters and under random initial configurations of the forestry crane. In addition, reward functions and reinforcement learning baselines are investigated to provide an open-source benchmark for the community in large-scale manipulation tasks.