Episodic Training

Episodic training is a machine learning paradigm where models are trained on small, self-contained datasets (episodes), mimicking the way humans learn from distinct experiences. Current research focuses on improving sample efficiency and generalization by exploring variations in episode design, optimizing replay ratios, and integrating techniques like contrastive learning and transfer learning within episodic frameworks, often employing neural networks such as transformers and neural processes. This approach holds significant promise for advancing few-shot learning, multi-agent reinforcement learning, and other areas requiring efficient learning from limited data, ultimately leading to more robust and adaptable AI systems.