Demonstration-Augmented Deep Reinforcement Learning for Robotic Task Optimization: A Framework for Enhanced Learning Efficiency and Precision

Deep Reinforcement Learning (DRL) enables robots to learn complex tasks autonomously. However, its reliance on random exploration and sparse reward signals often leads to inefficient learning and suboptimal performance. This paper presents a framework that integrates expert-generated data into the model-free DRL training process to improve learning efficiency and task precision. The proposed method introduces expert demonstrations into the replay buffer during training and dynamically adjusts their sampling to facilitate a smooth transition from expert-guided learning to autonomous policy optimization. Unlike approaches that rely on pre-collected expert datasets, this framework provides expert data online during training, which allows adaptive guidance based on task requirements. The approach is evaluated in robotic tasks involving sequential decision-making and object interaction. The results demonstrate that integrating expert data enhances learning efficiency and makes it an effective strategy for training DRL agents in structured environments.