Acquiring new knowledge often requires an agent or a system to explore, search and discover. Yet us humans build upon the knowledge of our forefathers, as did they, using previous knowledge; there does exist a mechanism which allows transference of knowledge without searching, exploration or discovery. That mechanism is known as imitation and it exists everywhere in nature; in animals, insects, primates, and humans. Enabling artificial, cognitive and software agents to learn by imitation could potentially be crucial to the emergence of the field of autonomous systems, robotics, cyber-physical and software agents. Imitation in AI implies that agents can learn from their human users, other AI agents, through observation or using physical interaction in robotics, and therefore learn a lot faster and easier. Describing an imitation learning framework in AI which uses the Internet as the source of knowledge requires a rather unconventional approach: the procedure is a temporal-sequential process which uses reinforcement based on behaviouristic Psychology, deep learning and a plethora of other Algorithms. Ergo an agent using a hybrid simulating-emulating strategy is formulated, implemented and experimented with. That agent learns from RSS feeds using examples provided by the user; it adheres to previous research work and theoretical foundations and demonstrates that not only is imitation learning in AI possible, but it compares and in some cases outperforms traditional approaches.