Identifying environmentally sustainable pavement management strategies via deep reinforcement learning

Pavement life cycle assessments (LCAs) enable decision-makers to evaluate the environmental impact of alternative maintenance, rehabilitation, and reconstruction strategies. This paper explores the viability of deep reinforcement learning (DRL), a framework that enables agents to learn optimal actions within a given situation, to identify environmentally benign pavement management strategies. More specifically, this study utilizes proximal-policy optimization (PPO), a subtype of DRL algorithms, to identify a management strategy that minimizes the expected global warming impact of a pavement facility over its lifecycle. Through an urban Interstate case study, this paper shows that the proposed PPO algorithm identifies management strategies that are anticipated to reduce the expected global warming impact of a pavement facility over its planning horizon by 16 percent relative to traditional practice. Furthermore, the PPO algorithm is able to identify this management strategy in only 25 learning iterations, which is in stark comparison to Q-learning, a common reinforcement learning algorithm, that requires 70,000 learning iterations. The results of this work highlight the viability of DRL to integrate within complex LCA models to determine environmentally sustainable pavement management strategies. •Deep reinforcement learning (DRL) supports the identification of environmentally benign pavement management strategies.•Probabilistic LCA integrates DRL to minimize a pavement's global warming impact.•Proximal policy optimization (PPO) converges to a near-optimal management strategy.•PPO identifies a management strategy where expected GWP is 16% lower than traditional practice.