In the context of soccer robotics, ball dribbling is a complex behavior where a robot player attempts to maneuver the ball in a very controlled way, while moving towards a desired target. To learn when and how to modify the robot's velocity vector is a complex problem, hardly solvable in an effective way with methods based on identification of the system dynamics and/or kinematics and mathematical models. We propose a decentralized reinforcement learning strategy, where each component of the omnidirectional biped walk (vx, vy, Vθ) is learned in parallel with single-agents working in a multi-agent task. Moreover, we propose an approach to accelerate the decentralized learning based on knowledge transfer from simple linear controllers. Obtained results are successful; with less human effort, and less required designer knowledge, the decentralized reinforcement learning scheme shows better performances than the current drib-bling engine used by UChile Robotics Team in the SPL robot soccer competitions. The proposed decentralized reinforcement learning scheme achieves asymptotic performance after 1500 episodes and can be accelerated up to 70% by using our approach to share actions.