Key points are not available for this paper at this time.
In the process of multi-UAV reconnaissance and exploration, the effective rewards given to intelligent agents by the environment are too sparse, while standard reinforcement learning algorithms perform poorly in environments with sparse feedback to intelligent agents, specifically manifested as not actively exploring the environment. A curiosity driven reinforcement learning algorithm (ICM-IDQN) combining intrinsic motivation learning is proposed to address the problem of sparse environmental rewards. After experimental verification, this method can obtain more rewards in sparse environments, accelerate convergence, and increase exploration performance.
Building similarity graph...
Analyzing shared references across papers
Loading...
Huang et al. (Wed,) studied this question.
www.synapsesocial.com/papers/68e6a273b6db6435876259ca — DOI: https://doi.org/10.1109/ichms59971.2024.10555640
Jingyi Huang
Shuying Wu
Ziyi Yang
Northwestern Polytechnical University
Moscow Aviation Institute
Building similarity graph...
Analyzing shared references across papers
Loading...
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: