Conventional vehicles with internal combustion engine (ICE) provide a good performance and long operating range by utilizing the high energy-density advantages of petroleum fuels. However, the conventional ICE-vehicles suffer the disadvantages of poor fuel economy and environmental air pollution. One of the immediate alternative solutions is the HEVs (Hybrid-Electric vehicle). In HEV, the introduction of one or more power sources increases the complexity of powertrain architecture and offers additional degrees of freedom in controlling the power-split between the power sources. In this energy management scenario, the Reinforcement learning (RL) allows to obtain a global optimization implemented in real-time differently from rule-based or optimization-based control strategies.