NeuroGenesis: Agent That Learns to Perform Multiple Household Tasks

This research introduces NeuroGenesis, an intelligent household agent powered by Reinforcement Learning (RL) that can learn and execute a variety of domestic tasks through continuous adaptation and autonomous decision-making. The proposed system demonstrates how learning-based intelligence can overcome the limitations of conventional smart home automation by enabling more flexible, responsive, and context-aware behavior. Experimental results obtained from the simulation environment confirm the effectiveness of the approach in achieving reliable task execution, adaptability to changing conditions, and scalability for handling multiple household activities. Looking ahead, future work will focus on extending the system beyond simulation into real-world household environments. Additional research directions include enhancing collaboration among multiple intelligent agents, improving task coordination in complex scenarios, and incorporating Explainable Artificial Intelligence (XAI) techniques to provide greater transparency and user understanding of the agent’s decisions. These advancements will contribute to building more trustworthy, efficient, and user-centric smart home systems, as highlighted in previous studies [3].

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