Intelligent proximal-policy-optimization-based decision-making system for humanoid robots

With the advancements in technology, robots have gradually replaced humans in different aspects. Allowing robots to handle multiple situations simultaneously and perform different actions depending on the situation has since become a critical topic. Currently, training a robot to perform a designated action is considered an easy task. However, when a robot is required to perform actions in different environments, both resetting and retraining are required, which are time-consuming and inefficient. Therefore, allowing robots to autonomously identify their environment can significantly reduce the time consumed. How to employ machine learning al-gorithms to achieve autonomous robot learning has formed a research trend in current studies. In this study, to solve the aforementioned problem, a proximal policy optimization algorithm was used to allow a robot to conduct self-training and select an optimal gait pattern to reach its destination successfully. Multiple basic gait patterns were selected, and information-maximizing generative adversarial nets were used to generate gait patterns and allow the robot to choose from numerous gait patterns while walking. The experimental results indicated that, after self-learning, the robot successfully made different choices depending on the situation, verifying this approach's feasibility.