Strengthening Adaptive Agents Managing Unstructured Environments for Learning in the Wild

Abstract

In dynamic real-world settings, artificial agents must adapt and learn from unstructured, unpredictable environments—collectively referred to as "learning in the wild." Unlike controlled laboratory conditions, these environments pose challenges such as partial observability, non-stationary distributions, unexpected noise, and sparse feedback. This paper presents a novel adaptive framework that strengthens agent resilience and learning capability in such conditions. The proposed approach integrates reinforcement learning with meta-learning and self-supervised representation learning to create agents that can generalize across diverse, evolving tasks. We incorporate a multi-scale memory module and adaptive exploration strategies, enabling agents to retain useful context while continuously adjusting to environmental shifts. Experimental evaluations were conducted on modified OpenAI Gym environments and real-world robotic interaction datasets, demonstrating marked improvements in policy robustness, sample efficiency, and transferability. Our results show that agents trained under this hybrid framework not only learn faster but also retain competence when introduced to unfamiliar or chaotic scenarios. Furthermore, we discuss the implications of deploying such agents in open-world applications, such as autonomous navigation, disaster response, and environmental monitoring. This work contributes to the development of more resilient, scalable AI systems capable of thriving in the wild—mirroring the adaptability of biological intelligence.