Transfer Learning
Transfer learning is a machine learning technique where knowledge acquired during training on a source task is applied to improve performance on a different but related target task. Rather than training a model from scratch, this approach leverages pre-trained models or learned feature representations as a starting point. This method significantly reduces computational cost and data requirements, making it particularly valuable when the target task has limited training data available.
The process typically begins with training a model on a large, general dataset, such as ImageNet for computer vision or a massive corpus of text for natural language processing. The resulting weights or feature extractors capture general patterns and structures inherent in the data. These learned representations are then adapted to the specific target domain, often through fine-tuning, where the model’s parameters are updated using a smaller, task-specific dataset.
In the context of AI agents, transfer learning enables systems to generalize skills across different environments or objectives. By reusing previously learned policies or value functions, agents can achieve faster convergence and better sample efficiency. This is especially critical in reinforcement learning scenarios where data collection is expensive or dangerous, allowing agents to apply strategies from simulated or related real-world tasks to new, unseen situations.