IRIS: An information path planning method based on reinforcement learning and information-directed sampling

Abstract

Information Path Planning (IPP) is a critical aspect of robotics, aimed at intelligently selecting information-rich paths to optimize robot trajectories and significantly enhance the efficiency and quality of data collection. However, in the process of maximizing information acquisition, IPP must also account for energy consumption, time constraints, and physical obstacles, which often lead to inefficiencies. To address these challenges, we propose an Information Path Planning method based on Reinforcement Learning and Information-Directed Sampling (IRIS). This model is the first to integrate Reinforcement Learning (RL) with Information-Directed Sampling (IDS), ensuring both immediate rewards and the potential for greater information gain through exploratory actions. IRIS employs an off-policy deep reinforcement learning framework, effectively overcoming the limitations observed in on-policy methods, thereby enhancing the model’s adaptability and efficiency. Simulation results demonstrate that the IRIS algorithm performs exceptionally well across various IPP scenarios. Once training stabilizes, IDS will dominate decision-making with a probability of approximately 1.3 % to yield better outcomes, highlighting its significant potential in this field. The relevant code is available at https://github.com/SUTLZY/IRIS.