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Robots are increasingly operating in highly complex and dynamic scenarios where they must continuously perceive their environment, learn from new experiences, and apply acquired knowledge to complete their tasks effectively. In these environments, the potential situations a robot encounters can become too vast to handle with predefined conditions. As a result, autonomous robots must incorporate learning methods that accurately represent the environment, make informed decisions, and optimize learning speed, task performance, and computational resources. Given the recent advancements of Deep Reinforcement Learning over classical Reinforcement Learning, this paper presents a Deep Reinforcement Learning system for biologically inspired, socially-driven decision-making in autonomous robots operating in such intricate environments with countless variations. This work formulates a learning framework as a Markov Decision Process, enabling robots to demonstrate adaptive social behaviour by integrating internal and external factors. The robot’s state includes 11 variables derived from the robot’s motivations, user perception, ambient light, and social norms, allowing the robot to select from ten possible actions autonomously. This study aims to develop fully autonomous robots that operate autonomously, learning and adapting to complex environments while maintaining an optimal balance between the robot’s internal and social well-being. We compare eight state-of-the-art DRL algorithms to identify the best-performing approach and implement the learning system into our Mini social robot. The results highlight Rainbow as the most effective solution, enabling the Mini robot to exhibit highly adaptive, autonomous behaviour in challenging social environments. These results allow autonomous robots to increase their capabilities and reduce human supervision. |
