Neuroevolution of hybrid neural networks in a robotic agent (NRNH-AR)
Abstract
A Robotic Agent capable of learning from the dynamic environment through which it navigates has been developed, which aims to find a specific object. For the growth of their learning, the Neuroevolution of Hybrid Neural Networks in a Robotic Agent (NRNH-AR) has been created that combines networks such as CNN to understand the environment and ANN to perform actions, this is complemented by Deep Deterministic Policy Gradient (DDPG) composed by Deep Reinforcement Learning and Policy Gradient. However, for the algorithm to be successful practically in a physical robot, two blocks have also been considered: the Hardware and the mechanics involved, as it will be trained online to avoid latency problems and bandwidth limitation. With this research, it has been shown that with NRNH-AR it is possible to implement Deep Reinforcement Learning within a Robot, performing edge computing, in which there is not latency problem, optimizing time and computational cost through an evolutionary learning.
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