Yan-Ting Li

Less Repetition, Less Energy Cost: A Reinforcement Learning-Based Multiagent Energy-Saving Autonomous Exploration System.

This work proposes a distributed Multiagent Energy-saving Autonomous Exploration System (MEAES), and devise the consumption-exploration-balanced training framework (CEBF), which guides agents to transform from lazy exploration to energy-saving exploration strategies through dynamic reward shaping.