This thesis investigates the application of Multi-Agent Reinforcement Learning (MARL) to the development of a robust and adaptive agent able to interact with a partially observable and continuously evolving environment, while competing against other agents in order to achieve winning conditions. With the widespread adoption of deep learning, Reinforcement Learning (RL) has gained lots of popularity in the last decade, scaling to previously intractable problems, such as playing complicated games from pixel observations, sustaining conversations with humans and autonomous driving. However, there is still a wide range of domains inaccessible to RL due to the high computational cost of training or unfeasibility of agent convergence for complex problems.