mlsanityData to 13 Oct 2026

Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Driving

arXiv 2208.10302 ↗First posted 22 Aug 2022cs.RO cs.SY eess.SYcited by 6

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

Event-triggered model predictive control (eMPC) is a popular optimal control method with an aim to alleviate the computation and/or communication burden of MPC. However, it generally requires priori knowledge of the closed-loop system behavior along with the communication characteristics for designing the event-trigger policy. This paper attempts to solve this challenge by proposing an efficient eMPC framework and demonstrate successful implementation of this framework on the autonomous vehicle path following. First of all, a model-free reinforcement learning (RL) agent is used to learn the optimal event-trigger policy without the need for a complete dynamical system and communication knowledge in this framework. Furthermore, techniques including prioritized experience replay (PER) buffer and long-short term memory (LSTM) are employed to foster exploration and improve training efficiency. In this paper, we use the proposed framework with three deep RL algorithms, i.e., Double Q-learning (DDQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC), to solve this problem. Experimental results show that all three deep RL-based eMPC (deep-RL-eMPC) can achieve better evaluation performance than the conventional threshold-based and previous linear Q-based approach in the autonomous path following. In particular, PPO-eMPC with LSTM and DDQN-eMPC with PER and LSTM obtains a superior balance between the closed-loop control performance and event-trigger frequency. The associated code is open-sourced and available at: https://github.com/DangFengying/RL-based-event-triggered-MPC.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 19 of its 78 references that are in the map. First posted on arXiv 22 Aug 2022.

Builds on

19 of 78 references.

References · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 14,592
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,656
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,221
  4. 2015Trust Region Policy Optimizationcited by 2,513
  5. 2017CARLA: An Open Urban Driving Simulatorcited by 2,439
  6. 2015Prioritized Experience Replaycited by 1,078
  7. 2020Revisiting Fundamentals of Experience Replaycited by 173
  8. 2019Soft Actor-Critic for Discrete Action Settingscited by 165
  9. 2017Cautious Model Predictive Control using Gaussian Process Regressioncited by 91
  10. 2017Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Controlcited by 50
  11. 2019Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Controlcited by 19
  12. 2018Deep Reinforcement Learning for Event-Triggered Controlcited by 16
7 more
  1. 2020Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robotcited by 15
  2. 2019Deep Value Model Predictive Controlcited by 14
  3. 2019Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Controlcited by 12
  4. 2020Machine Learning in Event-Triggered Control: Recent Advances and Open Issuescited by 4
  5. 2020Optimization of the Model Predictive Control Update Interval Using Reinforcement Learningcited by 2
  6. 2019Event-triggered Pulse Control with Model Learning (if Necessary)cited by 1
  7. 2020Computationally efficient stochastic MPC: a probabilistic scaling approachcited by 1

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Drivingcited by 4
  2. 2024Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learningcited by 2
  3. 2025Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policiescited by 2
  4. 2025MIND-Stack: Modular, Interpretable, End-to-End Differentiability for Autonomous Navigationcited by 0
  5. 2026Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learningcited by 0
  6. 2026Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learningcited by 0

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  2. 2023Learning the References of Online Model Predictive Control for Urban Self-Driving
  3. 2021Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving
  4. 2023Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control
  5. 2025Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles
  6. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  7. 2023Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
  8. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving