Cross-Domain Knowledge Transfer Algorithms For Rapid Agent Adaptation In New Environments

Abstract

A major challenge in AI and machine learning is the ability to quickly adapt an intelligent agent to a new, and often very different, environment. In dynamic environments, conventional reinforcement learning and supervised learning methods tend to rely on a lot of task-specific data and lengthy training periods, which can hinder efficiency. In this paper, we propose a novel cross-domain knowledge transfer framework that enhances the adaptation speed of agents by combining domain-invariant feature extraction, the reuse of policies, and structured knowledge distillation. The framework allows for the effective transfer of knowledge from past experiences, while also reducing "negative transfer" of information as the agents learn in a new environment. The experiments were run on the modified version of LunarLander, CartPole, and MountainCar tasks, along with the image-based cross-domain tasks using MNIST and Fashion-MNIST datasets. The following measures were used to evaluate: cumulative reward, task success rate, adaptation speed, and computational efficiency. The proposed framework demonstrated a consistent improvement over the standard reinforcement learning, fine-tuning, and MAML-based adaptation methods, with the average task success rate of 92%, cumulative rewards of up to 220, and a decreased number of adaptation episodes by over 50%. The ablation studies further demonstrated that all the components are important and make a large impact on improving learning efficiency, especially domain-invariant feature extraction. The outcomes demonstrated the framework's ability to transfer knowledge to a variety of environments, suggesting its potential for use in robotics, autonomous systems, and multi-agent coordination. Going forward, the work will include scaling to highly divergent domains, adaptive weighting strategies, and real-world deployments to assess robustness and applicability further.