To address this problem, we propose a double-agent Q-learning algorithm for an intelligent traffic control system, specifically to handle two intersections. In the proposed method, we employ two ...

CS4246/ ├── algorithms/ │ ├── DQN/ │ ├── PCQL/ │ ├── PPO/ │ ├── PPO-LSTM/ │ ├── Q-Learning/ │ ├── SARSA ...

This paper proposed a multi-agent reinforcement learning algorithm for traffic signal control and developed a general multi-agent optimization simulation tool to evaluate different signal control ...

A reinforcement learning algorithm, such as Q-learning or the Deep Q-Network (DQN), is employed to update the agent's policy based on its experiences. DRL offers a promising approach to addressing the ...

Institute of Atmospheric Pollution Research (IIA), National Research Council, Rome, Italy Adaptive traffic signal control (ATSC) in urban traffic networks poses a challenging task due to the ...

Southwest Research Institute, in collaboration with Vanderbilt University, is developing machine learning algorithms ... with dynamic lane control, speed harmonization, traffic signal control ...

A reward shaping deep deterministic policy gradient (RS-DDPG) and simultaneous localization and mapping (SLAM) path tracking algorithm is proposed to address the issues of low accuracy and poor ...

most people would not conclude you are smart ... that were learning to grasp items and teach others how to do the same — which was one early way for a reinforcement learning algorithm to ...