Fifteen percent of Germans suffer from anxiety disorders. Current treatments make intense use of exposure therapy to extinguish fearful behavior in patients. Unfortunately, the seemingly extinguished fear might renew, either spontaneously or due to changes in the environmental context, when the patient leaves the doctor’s office. Therefore, studying the neural mechanisms of context-dependent fear extinction and renewal is important for understanding anxiety disorders and their treatment in human patients. While the hippocampus is known to play a major role in fear renewal, the neural basis of this phenomenon, and especially its context-dependency, are still not fully understood. To address this issue, we developed a computational model of spatial learning in rodents based on modern machine learning techniques, including reinforcement learning with experience replay (keras-rl), and deep neural networks (keras, TensorFlow). This model controls the behavior of artificial agents that explore complex virtual environments (Blender, Unity). Fear learning and extinction can be easily induced in our model, and modification of the virtual environments results in visual context changes that cause the agents to show fear renewal. Furthermore, we analyze the context-dependency of renewal by scrutinizing the deep neural networks trained during the simulations, comparing the results to the neural correlates of extinction learning in rodents. Within this larger endeavor, projects at the Bachelors and Masters level will focus on developing new modules in the computational framework and/or studying specific scientific questions about spatial navigation, spatial learning, extinction learning and renewal.