Collision Avoidance Robotics Via Meta-Learning (CARML)

Inspired by the work done by Andrychowicz et al. in [7], they modeled an I. INTRODUCTION LSTM as a meta-learner, which helped to train another neural Today, most deep reinforcement learning techniques require network "learner" classifier using a few-shot framework. Unlike models to be trained on a large number of training samples. In common deep learning optimizers such as Momentum, contrast, Model-Agnostic Meta-Learning (MAML) proposed ADAM, and Adagrad, this method is able to train a model by Finn et.