Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic

• URL: http://arxiv.org/abs/2312.15163v1
• Date: Sat, 23 Dec 2023 04:51:23 GMT
• Title: Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic
• Authors: Elizabeth Akinyi Ondula, Bhaskar Krishnamachari
• Abstract summary: This research focuses on reinforcement learning (RL) to develop strategies that balance minimizing infections with maximizing in-person interactions in educational settings. We introduce SafeCampus, a novel tool that simulates infection spread and facilitates the exploration of various RL algorithms.
• Score: 9.68145635795782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Epidemic modeling, encompassing deterministic and stochastic approaches, is vital for understanding infectious diseases and informing public health strategies. This research adopts a prescriptive approach, focusing on reinforcement learning (RL) to develop strategies that balance minimizing infections with maximizing in-person interactions in educational settings. We introduce SafeCampus , a novel tool that simulates infection spread and facilitates the exploration of various RL algorithms in response to epidemic challenges. SafeCampus incorporates a custom RL environment, informed by stochastic epidemic models, to realistically represent university campus dynamics during epidemics. We evaluate Q-learning for a discretized state space which resulted in a policy matrix that not only guides occupancy decisions under varying epidemic conditions but also illustrates the inherent trade-off in epidemic management. This trade-off is characterized by the dilemma between stricter measures, which may effectively reduce infections but impose less educational benefit (more in-person interactions), and more lenient policies, which could lead to higher infection rates.

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