Mindful Human Digital Twins: Integrating Theory of Mind with multi-agent reinforcement learning

Abstract

Multi-Agent Reinforcement Learning (MARL) is focused on enabling autonomous agents to learn and adapt to complex environments through interactions with their surroundings and other agents. A key challenge in MARL is developing agents with the human-like capacity to understand, predict, and respond to the intentions and mental states of their peers. This capability, commonly referred to as the Theory of Mind (ToM), is central to fostering more sophisticated and realistic interactions among autonomous agents. In this paper, we propose a novel approach that leverages Theory-Theory (TT) and Simulation-Theory (ST) to enhance ToM within the MARL framework. Building on the Digital Twins (DT) framework, we introduce the Mindful Human Digital Twin (MHDT). These intelligent systems enriched with ToM capabilities bridge the gap between artificial agents and human-like interactions. In this work, we utilized OpenAI Gymnasium to perform simulations and evaluate the effectiveness of our approach. This work represents a significant step forward in Artificial Intelligence (AI), resulting in socially intelligent systems capable of natural and intuitive interactions with both their environment and other agents. This approach is particularly effective in addressing critical social challenges such as school bullying. This research not only advances the growing field of MARL but also paves the way for sophisticated AI systems with enhanced ToM abilities, tailored for complex and sensitive real-world applications.