Self-Evolving Autonomous AI Agents for Lifelong Learning in Dynamic Multi-Domain Environments

Abstract

ABSTRACT Autonomous artificial intelligence agents operating in non-stationary, multi domain environments face a fundamental trade-off between retaining previously acquired knowledge and rapidly adapting to novel task dynamics. Traditional deep reinforcement learning and deep neural architectures suffer severely from catastrophic forgetting, rigid policy plasticity, and capacity exhaustion when deployed across heterogeneous domains. In this paper, we propose the Self-Evolving Autonomous Agent for Lifelong Learning (SEAL) framework—a unified meta-evolutionary paradigm that synergizes dynamic neural expansion, generative episodic memory replay, and constrained evolutionary structural mutation. The SEAL framework enables an agent to autonomously adapt its underlying deep network topology, preserve historical task representations through structural consolidation, and discover specialized sub-networks without requiring manual architectural intervention or global retraining. We conduct rigorous empirical evaluations across a five stage multi-domain pipeline encompassing robotic locomotion, financial algorithmic trading, clinical healthcare diagnostics, cyber-threat detection, and autonomous vehicle navigation. Experimental results demonstrate that the SEAL framework achieves a 91.2% average accuracy retention across all historical domains, outperforming Elastic Weight Consolidation (EWC) by 33.0%, Dark Experience Replay (DER++) by 18.1%, and standard Deep RL by 72.8%. Furthermore, the proposed meta-evolutionary structural mutation operator reduces parametric memory growth by 42.6% compared to naive dynamic expansion baselines. This study establishes a robust scalable foundation for autonomous, self-sustaining lifelong learning agents deployed in complex real-world open-world settings.

KEYWORDS: Self-Evolving AI, Autonomous Agents, Lifelong Learning, Continual Learning, Catastrophic Forgetting, Evolutionary Neural Architecture Search, Dynamic Multi-Domain Environments.