Traffic signal control (TSC) is a challenging problem within intelligent transportation systems and has been studied using multi-agent reinforcement learning (MARL). While centralized approaches are often infeasible for large scale TSC problems, decentralized approaches provide scalability but introduce new challenges, such as partial observability. Communication plays a critical role in decentralized MARL as agents must learn to encode information in messages to better understand the system and achieve effective coordination. Deep MARL has been used to enable inter-agent communication by learning communication protocols in a differentiable manner. However, many deep MARL communication frameworks proposed for TSC allow agents to communicate with all other agents at all times, which can add to the existing noise in the system and degrade the overall performance. In this study, we propose a communication-based MARL framework for large-scale TSC. Our framework allows each agent to learn a communication policy that dictates "which" part of the message is sent "to whom". This results in a decentralized and flexible communication mechanism where agents can effectively use the communication channel only when necessary. We designed two networks, a synthetic 4 × 4 grid network and a real-world network based on the Pasubio neighborhood in Bologna. Our framework achieved the lowest network congestion compared to related methods, with agents utilizing ~ 47 – 65% of the communication channel. Ablation studies further demonstrated the effectiveness of the communication policies learned within our framework.

Ocean acidification (OA) reduces the concentration of seawater carbonate ions that stony corals need to produce their calcium carbonate skeletons and is considered a significant threat to the functional integrity of coral reef ecosystems. However, detection and attribution of OA impact on corals in nature are confounded by concurrent environmental changes, including ocean warming. Here we use a numerical model to isolate the effects of OA and temperature and show that OA alone has caused 13 ± 3% decline in the skeletal density of massive Porites corals on the Great Barrier Reef since 1950. This OA‐induced thinning of coral skeletons, also evident in Porites from the South China Sea but not in the central Pacific, reflects enhanced acidification of reef water relative to the surrounding open ocean. Our finding reinforces concerns that even corals that might survive multiple heatwaves are structurally weakened and increasingly vulnerable to the compounding effects of climate change

Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face challenges such as slow simulation speeds and convoluted, difficult-to-maintain codebases. To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. PyTSC integrates multiple simulators, such as SUMO and CityFlow, and offers a streamlined API, enabling researchers to explore a broad spectrum of MARL approaches efficiently. PyTSC accelerates experimentation and provides new opportunities for advancing intelligent traffic management systems in real-world applications.