Meta-learning, or learning to learn, has become well-known in the field of artificial intelligence as a technique for improving the learning performance of learning algorithms. It has been used to uncover the learning principles that allow learned models to effectively adapt and generalise to new tasks after deployment. Meta-learning via meta-loss learning is a framework that is used to train loss or reward functions that improve the sample efficiency, learning stability, and convergence speed of models trained under them. One of the models that can be improved using this framework is Neural Dynamic Policies (NDPs), which are made up of a deep neural network and a dynamical system. They can be used to predict trajectories given high-dimensional inputs, such as images. The objective of this thesis is to learn loss functions to speed up and stabilize the training process of complex policies. Specifically, this work aims to investigate the possibility of enhancing the performance of Neural Dynamic Policies using a meta-learning method for learning parametric loss functions in both supervised and reinforcement learning settings. To this end, the task is to learn to draw numbers using the S-mnist dataset and the results show that NDPs trained on the newly learned loss outperforms the baseline in terms of learning speed and sample efficiency.