Summary Traffic Light Control with Reinforcement Learning arxiv.org
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This paper proposes a real-time traffic light control method using deep Q learning, with a reward function that considers queue lengths, delays, travel time, and throughput, and involves an offline stage with pre-generated data and a fixed schedule for training.
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Key Points
- Real-time traffic light control method using deep Q learning
- Incorporates reward function considering queue lengths, delays, travel time, and throughput
- Self-organizing traffic lights that use real-time traffic data
- Reinforcement learning (RL) for adapting to changing traffic conditions
- Deep Q network (DQN) for approximating the Q value function
- Objective is to maximize traffic flow while minimizing delays and congestion
- Comparison of deep Q learning algorithm to traditional fixed signal plans
- Various studies conducted on traffic light control using reinforcement learning
Summaries
42 word summary
This paper suggests a real-time traffic light control method that uses deep Q learning. It includes a reward function considering queue lengths, delays, travel time, and throughput. The training consists of an offline stage with pre-generated data and a fixed schedule, followed
43 word summary
This paper proposes a real-time traffic light control method using deep Q learning. The approach incorporates a reward function considering queue lengths, delays, travel time, and throughput. The training involves an offline stage from pre-generated data with fixed schedules and an online stage
345 word summary
This paper proposes a real-time traffic light control method using deep Q learning. The approach incorporates a reward function considering queue lengths, delays, travel time, and throughput. The training involves an offline stage from pre-generated data with fixed schedules and an online stage
Self-organizing traffic lights that use real-time traffic data can handle highly random traffic conditions. Fixed-time schedules are limited because they cannot adapt to changing traffic conditions, leading to inefficient traffic flow. Reinforcement learning (RL) is an area of machine
The Q value for the current state-action pair is updated using a fraction of the temporal difference error. Deep neural networks can be used to approximate the Q value function in cases of high-dimensional or continuous state spaces. The Deep Q network (DQN)
The study focuses on traffic light control using reinforcement learning. The action set consists of changing to the next phase or keeping the current phase. The objective is to maximize traffic flow while minimizing delays and congestion. The reward function is a weighted sum of factors such
This study focuses on traffic light control using reinforcement learning. The agent randomly selects an action with a probability of ε, otherwise it selects the action with the highest Q-value. After taking an action, the agent observes the environment and receives a reward. The
This summary presents the key points from the excerpted text on traffic light control with reinforcement learning. The study compares a deep Q learning algorithm to traditional fixed signal plans for traffic light control. The algorithm uses a reward function that considers queue lengths, delays,
The number of motor vehicles in China has reached 417 million, with over 500 million drivers. Various studies have been conducted on traffic light control using reinforcement learning. One study focused on self-organizing traffic lights and provided a realistic simulation. Another
This article provides a list of references related to traffic light control and reinforcement learning. The references cover various topics such as path recommendations during public transit disruptions, capacity-constrained network performance models for urban rail systems, calibrating path choices and train capacities using