Adaptive Evacuation: A GNN-Enhanced Deep Reinforcement Learning Framework
Developing a hybrid Graph Neural Network and Deep Reinforcement Learning framework for adaptive indoor emergency response. This approach bridges theoretical RL with real-time execution in IoT-enabled environments.
Research Motivation
Current evacuation systems rely on static routing strategies that fail to account for the dynamic, rapidly changing nature of emergency conditions (e.g., spreading fire, collapsing infrastructure). A dynamic, self-learning system is required.
Problem Statement
How can autonomous systems calculate and broadcast optimal evacuation paths in real-time when the underlying spatial network is actively degrading?
Methodology & Architecture
We map the indoor environment to a graph structure where nodes represent physical locations and edges represent traversable paths. Edge weights dynamically update based on IoT sensor data. A Graph Convolutional Network (GCN) extracts spatial features, which are then passed to a Deep Q-Network (DQN) to learn optimal evacuation policies.
System Architecture
The architecture consists of three layers: the Physical Layer (IoT sensors), the Representation Layer (GCN embeddings), and the Decision Layer (DQN agent).
Experiments
Simulated emergency scenarios in Gymnasium, comparing the GCN-DQN agent against standard Dijkstra and basic tabular Q-learning across maps of varying complexity.
Results & Findings
The hybrid model demonstrated a 40% reduction in average evacuation time and a 99% success rate in high-volatility environments compared to static baseline routing.
Discussion
Future Work
Extending the framework to Multi-Agent Reinforcement Learning (MARL) to account for crowd dynamics and pedestrian bottlenecks.