Abstract: Generating a digital twin of a complex system including receiving at least one training dataset in which each sample includes information on a state and on associated action, including related time information, training a generative model over states, actions and time information to learn a topological space representing attainable system states, in an unsupervised fashion over those states, actions and time information, wherein the generative model learns the mapping to realistic samples includes the space and transitions associated with those samples subject to the actions, and outputting a digital twin including the topological space and transitions between the attainable states subject to the actions, for simulating behaviors of the system by the digital twin to properly achieve one or more tasks pertaining to the system. Applications to reinforcement learning, notably for biological cells.