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Intelligent traffic light control (ITLC) aims to relieve traffic congestion. Some multi-agent deep reinforcement learning (MADRL) algorithms have been proposed for ITLC, and most of them use deep neural networks to make decisions. However, the abundant parameters of deep structure lead to the time-consuming training process of MADRL. Recently, a broad reinforcement learning (BRL) approach has been proposed to improve the efficiency of training for an agent. Unlike MADRL algorithms that use deep architecture, BRL utilizes a broad architecture. In this paper, we propose a multi-agent broad reinforcement learning (MABRL) algorithm for ITLC. The MABRL algorithm adopts the broad network to process the joint information and updates the parameters using ridge regression. To increase the effectiveness of interaction among agents, we design a dynamic interaction mechanism (DIM) based on the attention mechanism. The DIM enables agents to aggregate information on particular intersections at appropriate moments. We conduct experiments on three different datasets. The results demonstrate that the effectiveness of MABRL outperforms several state-of-the-art algorithms in alleviating traffic congestion with shorter training time. [ABSTRACT FROM AUTHOR]

In this paper we consider the estimation of optimal state-action value function Q ⁎ with ReLU ResNet based on minimax Bellman error minimization. We construct the non-asymptotic error bounds for the minimax estimator and the estimated Q function induced by the estimated greedy policy. To bound the Bellman residual error, we guarantee the approximation errors based on deep approximation theory and the statistical ones by utilizing empirical processes taking into account the Markov decision process dependency. We provide a novel generalization bound with dependent data and an approximation bound in the Hölder class which are of independent interest. This bound depends on the sample size, the ambient dimension, the width and depth of the neural network, which can bring prior insights into tuning these hyper-parameters to achieve a desired convergence rate in practice. Furthermore, the bound circumvents the curse of dimensionality if the distribution of state-action pairs is assumed to be supported on a set of low intrinsic dimension. [ABSTRACT FROM AUTHOR]