DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance

• URL: http://arxiv.org/abs/2503.02279v1
• Date: Tue, 04 Mar 2025 05:02:46 GMT
• Title: DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance
• Authors: Qiang Li, Yinhan Lin, Qin Luo, Lina Yu,
• Abstract summary: Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies.<n>The DreamerV3 algorithm presents compelling properties for policy learning.<n>In this paper, a corridor TSC model is trained using the DreamerV3 algorithm to explore the benefits of world models for TSC strategy learning.
• Score: 4.962905815955427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies. However, current RL algorithms necessitate excessive interaction with the environment to learn effective policies, making them impractical for large-scale tasks. The DreamerV3 algorithm presents compelling properties for policy learning. It summarizes general dynamics knowledge about the environment and enables the prediction of future outcomes of potential actions from past experience, reducing the interaction with the environment through imagination training. In this paper, a corridor TSC model is trained using the DreamerV3 algorithm to explore the benefits of world models for TSC strategy learning. In RL environment design, to manage congestion levels effectively, both the state and reward functions are defined based on queue length, and the action is designed to manage queue length efficiently. Using the SUMO simulation platform, the two hyperparameters (training ratio and model size) of the DreamerV3 algorithm were tuned and analyzed across different OD matrix scenarios. We discovered that choosing a smaller model size and initially attempting several medium training ratios can significantly reduce the time spent on hyperparameter tuning. Additionally, we found that the approach is generally applicable as it can solve two TSC task scenarios with the same hyperparameters. Regarding the claimed data-efficiency of the DreamerV3 algorithm, due to the significant fluctuation of the episode reward curve in the early stages of training, it can only be confirmed that larger model sizes exhibit modest data-efficiency, and no evidence was found that increasing the training ratio accelerates convergence.

Related papers

• Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining [74.83412846804977]
  Reinforcement learning (RL)-based fine-tuning has become a crucial step in post-training language models. We present a systematic end-to-end study of RL fine-tuning for mathematical reasoning by training models entirely from scratch.
  arXiv  Detail & Related papers  (2025-04-10T17:15:53Z)
• MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning [17.437573206368494]
  Visual deep reinforcement learning (RL) enables robots to acquire skills from visual input for unstructured tasks. Current algorithms suffer from low sample efficiency, limiting their practical applicability. We present MENTOR, a method that improves both the architecture and optimization of RL agents.
  arXiv  Detail & Related papers  (2024-10-19T04:31:54Z)
• Optimization Hyper-parameter Laws for Large Language Models [52.49860340549727]
  We present Opt-Laws, a framework that captures the relationship between hyper- parameters and training outcomes.<n>Our validation across diverse model sizes and data scales demonstrates Opt-Laws' ability to accurately predict training loss.<n>This approach significantly reduces computational costs while enhancing overall model performance.
  arXiv  Detail & Related papers  (2024-09-07T09:37:19Z)
• PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning [13.564676246832544]
  We introduce PLANRL, a framework that chooses when the robot should use classical motion planning and when it should learn a policy. PLANRL switches between two modes of operation: reaching a waypoint using classical techniques when away from the objects and fine-grained manipulation control when about to interact with objects. We evaluate our approach across multiple challenging simulation environments and real-world tasks, demonstrating superior performance in terms of adaptability, efficiency, and generalization compared to existing methods.
  arXiv  Detail & Related papers  (2024-08-07T19:30:08Z)
• Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
  We take a closer theoretical look at Independent Subnetwork Training (IST) IST is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
  arXiv  Detail & Related papers  (2023-06-28T18:14:22Z)
• The Underlying Correlated Dynamics in Neural Training [6.385006149689549]
  Training of neural networks is a computationally intensive task. We propose a model based on the correlation of the parameters' dynamics, which dramatically reduces the dimensionality. This representation enhances the understanding of the underlying training dynamics and can pave the way for designing better acceleration techniques.
  arXiv  Detail & Related papers  (2022-12-18T08:34:11Z)
• Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels [112.63440666617494]
  Reinforcement learning algorithms can succeed but require large amounts of interactions between the agent and the environment. We propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent. We show robust performance on the Real-Word RL benchmark, hinting at resiliency to environment perturbations during adaptation.
  arXiv  Detail & Related papers  (2022-09-24T14:22:29Z)
• POAR: Efficient Policy Optimization via Online Abstract State Representation Learning [6.171331561029968]
  State Representation Learning (SRL) is proposed to specifically learn to encode task-relevant features from complex sensory data into low-dimensional states. We introduce a new SRL prior called domain resemblance to leverage expert demonstration to improve SRL interpretations. We empirically verify POAR to efficiently handle tasks in high dimensions and facilitate training real-life robots directly from scratch.
  arXiv  Detail & Related papers  (2021-09-17T16:52:03Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)
• Continual Model-Based Reinforcement Learning with Hypernetworks [24.86684067407964]
  We propose a method that continually learns encountered dynamics in a sequence of tasks using task-conditional hypernetworks. Our method has three main attributes: first, it includes dynamics learning sessions that do not revisit training data from previous tasks, so it only needs to store the most recent fixed-size portion of the state transition experience. We show that HyperCRL is effective in continual model-based reinforcement learning in robot locomotion and manipulation scenarios.
  arXiv  Detail & Related papers  (2020-09-25T01:46:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.