An improved Q-learning approach for rescue path planning in mass casualty incidents under damaged road network conditions.

Emergency rescue path planning faces severe challenges due to the uncertainty of the road network and the complexity of road damage following mass casualty incidents (MCIs) such as earthquakes. Traditional static path planning algorithms struggle to cope with such dynamic environments, while standard reinforcement learning methods often cause agents to fall into local optima, failing to meet rescue safety requirements. To address this dilemma, this paper proposes an improved Q-learning algorithm that integrates Adaptive Simulated Annealing (ASA) and Experience Replay (ER) mechanisms. The algorithm dynamically regulates the exploration degree via the ASA strategy, ensuring the agent can escape local traps in complex damaged environments. Furthermore, it incorporates the ER mechanism to reuse key path experiences, thereby improving convergence speed. Experimental results demonstrate that the proposed method demonstrates efficient online re-planning capabilities in response to structural environmental updates. It provides high-confidence decision support for time-critical post-disaster rescue, balancing the trade-off between accessibility and safety.