Generalizing Cooperative Eco-driving via Multi-residual Task Learning

• URL: http://arxiv.org/abs/2403.04232v1
• Date: Thu, 7 Mar 2024 05:25:34 GMT
• Title: Generalizing Cooperative Eco-driving via Multi-residual Task Learning
• Authors: Vindula Jayawardana, Sirui Li, Cathy Wu, Yashar Farid, Kentaro Oguchi
• Abstract summary: Multi-residual Task Learning (MRTL) is a generic learning framework based on multi-task learning. MRTL decomposes control into nominal components that are effectively solved by conventional control methods and residual terms. We employ MRTL for fleet-level emission reduction in mixed traffic using autonomous vehicles as a means of system control.
• Score: 6.864745785996583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends with a multitude of diverse traffic scenarios that are challenging for these planning algorithms. Model-free Deep Reinforcement Learning (DRL) presents a promising avenue in this direction, but learning DRL control policies that generalize to multiple traffic scenarios is still a challenge. To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning. We employ MRTL for fleet-level emission reduction in mixed traffic using autonomous vehicles as a means of system control. By analyzing the performance of MRTL across nearly 600 signalized intersections and 1200 traffic scenarios, we demonstrate that it emerges as a promising approach to synergize the strengths of DRL and conventional methods in generalizable control.

Related papers

• IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning [4.80862277413422]
  We propose IntersectionZoo, a comprehensive benchmark suite for multi-agent reinforcement learning. By grounding IntersectionZoo in a real-world application, we naturally capture real-world problem characteristics. IntersectionZoo is built on data-informed simulations of 16,334 signalized intersections from 10 major US cities.
  arXiv  Detail & Related papers  (2024-10-19T21:34:24Z)
• Reinforcement Learning with Model Predictive Control for Highway Ramp Metering [14.389086937116582]
  This work explores the synergy between model-based and learning-based strategies to enhance traffic flow management. The control problem is formulated as an RL task by crafting a suitable stage cost function. An MPC-based RL approach, which leverages the MPC optimal problem as a function approximation for the RL algorithm, is proposed to learn to efficiently control an on-ramp.
  arXiv  Detail & Related papers  (2023-11-15T09:50:54Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [87.1164964709168]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving [1.496194593196997]
  We propose a benchmark for generalizable reinforcement learning for autonomous driving. Our application-oriented benchmark enables a better understanding of the impact of design decisions. Our benchmark aims to encourage researchers to propose solutions that are able to successfully generalize across scenarios.
  arXiv  Detail & Related papers  (2022-07-23T06:29:43Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization [14.455497228170646]
  Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. This paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms.
  arXiv  Detail & Related papers  (2021-07-13T14:11:04Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control [54.162449208797334]
  Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. We propose a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
  arXiv  Detail & Related papers  (2021-01-04T03:06:08Z)
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control [65.00425082663146]
  We present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuous control. In KTM-DRL, the multi-task agent first leverages an offline knowledge transfer algorithm to quickly learn a control policy from the experience of task-specific teachers. The experimental results well justify the effectiveness of KTM-DRL and its knowledge transfer and online learning algorithms, as well as its superiority over the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2020-10-15T03:26:47Z)

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.