Sample Efficient Reinforcement Learning

Sample-efficient reinforcement learning (RL) aims to minimize the number of interactions needed for an agent to learn optimal behavior in an environment, addressing the significant data cost of traditional RL methods. Current research focuses on improving exploration strategies (e.g., optimistic Thompson sampling, active exploration guided by human feedback), leveraging structural properties of the environment (e.g., exploiting exogenous states, low-rank structure), and developing efficient representation learning techniques (e.g., implicit quantization, self-supervised learning with synthetic data). These advancements are crucial for deploying RL in real-world applications where data collection is expensive or dangerous, such as robotics and complex control systems.