Programming desired task solutions for modern complex systems is often challenging, since it relies on detailed system understanding. In such cases, learning from data can be a useful alternative. Reinforcement learning (RL) is a general approach to learn policies while interacting with the system. In this thesis, we investigate the use of RL for several industrial applications, such as control of a robot arm and a throttle valve, and propose RL approaches while addressing practical constraints.