Title: Multi-Reward Fusion:  Learning from Other Policies by Distilling . Abstract: Designing rewards is crucial for applying reinforcement learning in practice. However, it is  difficult to design a shaping reward which can accelerate agents' learning process without biasing the original task's optimization objective. Moreover, the low-dimensional representation of the reward and value function (i.e. scalar value) may also be an obstruction during the learning process. This paper contributes towards tackling these challenges, by proposing a new method, called Multi-Reward Fusion (MRF). MRF take as input  a list of human designed rewards, which contains the information from multiple perspectives about the task, and learns separate  policies for each component of the reward list. We formulate the problem of learning the target policy as a distillation task, propose a novel method which can selectively distills knowledge from the auxiliary policies, and theoretically show the feasibility of this method. We conduct extensive experiments and show that the MRF method performs better than state-of-the-art reward shaping methods.