Sample Efficient Policy

Sample-efficient policy learning in reinforcement learning aims to train effective robot policies with minimal data, addressing the high cost and time associated with traditional methods. Current research focuses on leveraging prior knowledge through techniques like imitation learning, Bayesian optimization, and reward shaping (e.g., using residual reward functions or learned reward models from video and language data), often incorporating model-based approaches or hybrid methods. These advancements are crucial for deploying robots in real-world scenarios where extensive data collection is impractical, impacting fields like robotics, AI, and human-robot interaction.