Neural Network Reinforcement Learning for Walking Control of a 3-Link Biped Robot

Abstract

In recent years, researches on adaptive control have focused on bio-inspired learning techniques to deal with real-life applications.Reinforcement Learning (RL) is one of these major techniques, which has been widely used in robot control approaches.The implementation of artificial neural networks in RL algorithms enables more efficient optimal control strategies.This article proposes a field application of neural network reinforcement learning (NNRL) for walking control of an active simulated 3-link biped robot.The adaptive control agent consists of two neural network units, known as actor and critic for learning prediction and learning control tasks.Results of the presented control method reveal its efficiency in stable walking control of the biped robot model as a nonlinear complex dynamic task.