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.

Removal Of Chromium From Electroplating Effluents By The Sulfide Process

A method has been developed for the removal of chromium using ferrous sulphide generated in situ. The effects of experimental parameters such as pH, reagent dosages, interference from cations and chelating agents have been investigated. Under optimum conditions, removal efficiencies of 99 and 97% for synthetic and industrial samples have been obtained. The method offers all the advantages of sulphide precipitation process and can be adopted easily for industrial effluents.