Multi-Task Reinforcement Meta-Learning in Neural Networks

Abstract

Artificial Neural Networks (ANN) is one of the main and widespread tools for creating intelligent systems. And, they are actively used for data analysis in many areas such as robotics, computer vision, natural language processing, etc. The learning process of ANN is one of the most labor-intensive stages in ANN. There are many different modifications of ANNs and methods for their training. Currently, deep neural networks are becoming one of the most popular methods of machine learning due to their effectiveness in areas such as speech recognition, medical informatics, computer vision, etc. It is known that ANN training depends on the type of input data. In this paper, reinforcement learning is considered, as popular method used in cases where information is reinforced by signals from the external environment with which the model interacts. The purpose of this paper is to develop a reinforcement meta-learning algorithm that would be efficient in terms of quality and speed of learning. However, despite the significant scientific progress in deep learning, existing algorithms are not efficient enough to solve problems in the real world. In addition, such algorithms require a significant amount of learning time, which complicates the development process. To solve these problems, the use of meta-learning or “learning to learn” algorithms has recently been especially relevant. The paper proposes an approach to reinforcement meta-learning using a multitasking weight optimizer. experimentally shown that the proposed approach is more efficient than the known MAML (Model-Agnostic Meta-Learning) algorithm. The proposed MAML SPSA-Track method shows an improvement in efficiency by an average of 4%, and MAML SPSA-Delta by 8%, respectively. Moreover, the last algorithm spends on average 2 times less time on push-v2 and pick-place-v2 tasks.