Accelerating Wound Healing using Deep Reinforcement Learning
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Description: We propose a deep reinforcement learning (DRL) based approach to expedite the wound healing process. Our method efficiently divides the vision-based control task into two distinct modules: a perception module and a controller module. By separating vision-based control into a perception module and a controller module, we train a DRL agent without sophisticated mathematical modeling. Our research demonstrates the algorithm’s convergence and establishes its robustness in accelerating the wound-healing process. The proposed DRL methodology showcases significant potential for expediting wound healing by effectively integrating perception, control, and predictive modeling, all while eliminating the need for intricate mathematical models.