Energy consumption efficiency during resource allocation is crucial for large scale networks. The evolution of Space Division Multiplexing Elastic Optical Networks (SDM-EONs) has presented tremendous spatial dimensions over single-mode networks to cope with the transmission of large traffic volumes, during peak load of the fluctuating traffic. However, due to large capacity transmission rates, the resource allocation challenges have led to incremental energy consumption. This is due to the impact of crosstalk (XT), which occurs between active adjacent cores, during core switching, and increasing service disruptions caused by rampant link failures. To address the above issues, this paper proposes a Distance-adaptive Energy-aware Resource Allocation (DERA) algorithm using a survival multipath scheme. The DERA algorithm implements the concept of the survival multipath scheme with Spectrum Compactness (SC) to minimize the impact of XT in SDM-EONs, during resource allocation. The objective is to improve spectral efficiency to save energy consumption, during peak load of the fluctuating traffic. During Routing, Modulation, Spectrum, and Core Allocation (RMSCA), the DERA algorithm can efficiently allocate resources in a survival multipath scheme. The performance of the DERA-RMSCA is compared with the two Multipath Protection (MPP) schemes, PRIME-RMSCA, and CMDE-RMSCA. The results demonstrate that DERA-RMSCA can achieve a better performance compared with PRIME-RMSCA, and CMDE-RMSCA.

Deep reinforcement learning (DRL) has superior autonomous decision-making capabilities, combining deep learning and reinforcement learning (RL). Unlike DRL employs deep neural networks (DNNs), broad RL (BRL) adopts the broad learning system (BLS) that is established with flat networks to generate the strategy. This article proposes the multiagent adaptive broad-DRL (ABDRL) approach for traffic light control (TLC), which combines the broad network with the deep network structure. Specifically, the structure of ABDRL first expands in the form of flatted broad networks. Then, the feature representation module that contains DNNs is employed to extract the critical traffic information. In addition, experiences sampled randomly by the experience replay mechanism cannot reflect the current training status of the agent effectively. In order to alleviate the impacts caused by random sampling, the forgetful experience mechanism (FEM) is incorporated into ABDRL. The FEM enables the agent to discriminate the importance of experiences stored in the experience reply buffer to improve robustness and adaptability. We validate the effectiveness of ABDRL in TLC, and the results illustrate the optimality and robustness of ABDRL over the state-of-the-art multiagent DRL (MADRL) algorithms.

Despite significant advancements in Multi-Agent Deep Reinforcement Learning (MADRL) approaches for Traffic Light Control (TLC), effectively coordinating agents in diverse traffic environments remains a challenge. Studies in MADRL for TLC often focus on repeatedly constructing the same intersection models with sparse experience. However, real road networks comprise Multi-Type of Intersections (MTIs) rather than being limited to intersections with four directions. In the scenario with MTIs, each type of intersection exhibits a distinctive topology structure and phase set, leading to disparities in the spaces of state and action. This paper introduces Adaptive Multi-agent Deep Mixed Reinforcement Learning (AMDMRL) for addressing tasks with multiple types of intersections in TLC. AMDMRL adopts a two-level hierarchy, where high-level proxies guide lowlevel agents in decision-making and updating. All proxies are updated by value decomposition to obtain the globally optimal policy. Moreover, the AMDMRL approach incorporates a mixed cooperative mechanism to enhance cooperation among agents, which adopts a mixed encoder to aggregate the information from correlated agents.We conduct comparative experiments involving four traditional and four DRL-based approaches, utilizing three training and four testing datasets. The results indicate that the AMDMRL approach achieves average reductions of 41% than traditional approaches, and 16% compared to DRL-based approaches in traveling time on three training datasets. During testing, the AMDMRL approach exhibits a 37% improvement in reward compared to the MADRL-based approaches.

Traffic signal control (TSC) provides a cost-effective approach to alleviating urban traffic congestion without requiring modifications to physical road infrastructure. As a classic multi-agent coordination problem, TSC can be optimized with multi-agent reinforcement learning (MARL). However, understanding the cooperative strategies among traffic signals is challenging due to the spatiotemporal dynamics inherent in distributed traffic signal controllers. Previous MARL approaches for TSC often rely on imprecise and redundant cooperation mechanisms, leading to excessive communication costs and inefficient training. In this study, we propose a novel dynamic and controllable cooperation MARL algorithm (DCoo) to optimize traffic signals coordination for alleviating traffic congestion. DCoo introduces a collaboration scheduling module (CSM) to obtain spatiotemporal collaborative information and incorporates a cooperative hyper-decision network (HDN) to optimize decisions by leveraging the processed information. In particular, the CSM first captures crucial temporal information for cooperation by analyzing the relationships between historical and current observations of intersections. CSM then frames the selection of spatial information as a binary classification problem to effectively expand the scope of collaboration among intersections. Additionally, HDN integrates an attention mechanism and permutation invariance to enhance the scalability of DCoo. We conduct comparative experiments on three real-world traffic datasets, and the results demonstrate that DCoo outperforms comparable algorithms in both training and testing phases.