Multi-agent Reinforcement Learning for Traffic Signal Control

Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users amidst the rapid increase in the usage of vehicles. In this paper, we formulate the TSC problem as a discounted cost Markov decision process (MDP) and apply multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies. We model each traffic signal junction as an independent agent. An agent decides the signal duration of its phases in a round-robin (RR) manner using multi-agent Q-learning with either is an element of-greedy or UCB 3] based exploration strategies. It updates its Q-factors based on the cost feedback signal received from its neighbouring agents. This feedback signal can be easily constructed and is shown to be effective in minimizing the average delay of the vehicles in the network. We show through simulations over VISSIM that our algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm 15] over two real road networks

Current trends and intricacies in the management of HIV-associated pulmonary tuberculosis

Human immunodeficiency virus (HIV) epidemic has undoubtedly increased the incidence of tuberculosis (TB) globally, posing a formidable global health challenge affecting 1.2 million cases. Pulmonary TB assumes utmost significance in the programmatic perspective as it is readily transmissible as well as easily diagnosable. HIV complicates every aspect of pulmonary tuberculosis from diagnosis to treatment, demanding a different approach to effectively tackle both the diseases. In order to control these converging epidemics, it is important to diagnose early, initiate appropriate therapy for both infections, prevent transmission and administer preventive therapy. Liquid culture methods and nucleic acid amplification tests for TB confirmation have replaced conventional solid media, enabling quicker and simultaneous detection of mycobacterium and its drug sensitivity profile Unique problems posed by the syndemic include Acquired rifampicin resistance, drug–drug interactions, malabsorption of drugs and immune reconstitution inflammatory syndrome or paradoxical reaction that complicate dual and concomitant therapy. While the antiretroviral therapy armamentarium is constantly reinforced by discovery of newer and safer drugs every year, only a few drugs for anti tuberculosis treatment have successfully emerged. These include bedaquiline, delamanid and pretomanid which have entered phase III B trials and are also available through conditional access national programmes. The current guidelines by WHO to start Antiretroviral therapy irrespective of CD4+ cell count based on benefits cited by recent trials could go a long way in preventing various complications caused by the deadly duo. This review provides a consolidated gist of the advancements, concepts and updates that have emerged in the management of HIV-associated pulmonary TB for maximizing efficacy, offering latest solutions for tackling drug–drug interactions and remedial measures for immune reconstitution inflammatory syndrome