Recent advancements have made Deep Reinforcement Learning (DRL) exceedingly more powerful, but the produced models remain very computationally complex and therefore difficult to deploy on edge devices.Compression methods such as quantization and distillation can be used to increase the applicability of DRL models on these low-power edge devices by decreasing the necessary precision and number of operations respectively. Training in low-precision is notoriously less stable however, which is ampli…