The purpose of this Master Thesis is to investigate and improve one of the state-of-the-art Safe Reinforcement Learning algorithms. The studied algorithm consists in the application of a Safety Layer to classical Reinforcement Learning algorithms in order to accomplish a Safe Exploration during learning phases, that would open up the doors of real-world training to intelligent agents. Safety Layer algorithm shows good performances in environments where the danger is located on the edges, but worsens when used in environments where the hazards permeate the space in an heterogenous way. To improve the performances in such peculiar situations, reward shaping has been introduced, in order to reinforce the safety action of Safety Layer.