The increase in the use of Artificial Intelligence (AI) algorithms in applications impacting human users and actors has, as a direct consequence, the need for endowing these AI systems with ethical behaviors. While several approaches already exist, the question of adaptability to changes in contexts, users behaviors or preferences still remains open. We propose to tackle this question using Multi-Agent Reinforcement Learning of ethical behavior in different situations using Q-Tables and Dynamic Self-Organizing Maps to allow dynamic learning of the representation of the environment’s state and reward functions to prescribe ethical behaviors. To evaluate this proposal, we developed a simulator of intelligent management of energy distribution in Smart Grids, evaluating different rewards functions to trigger ethical behaviors. Results show the ability to adapt to different conditions. Besides contributions on ethical adaptation, we compare our model to other learning approaches and show it performs better than a Deep Learning one (based on Actor-Critic).