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

arXiv: https://arxiv.org/abs/2208.10302 · HTML: https://mlsanity.com/paper/2208.10302/

## Key facts

- Cited by 7 papers in the map (data through 14 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.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Revisiting Fundamentals of Experience Replay](https://mlsanity.com/paper/2007.06700/) (2020)
- [Soft Actor-Critic for Discrete Action Settings](https://mlsanity.com/paper/1910.07207/) (2019)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control](https://mlsanity.com/paper/1706.06491/) (2017)
- [Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control](https://mlsanity.com/paper/1910.12047/) (2019)
- [Deep Value Model Predictive Control](https://mlsanity.com/paper/1910.03358/) (2019)
- [Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot](https://mlsanity.com/paper/2003.03200/) (2020)
- [Deep Reinforcement Learning for Event-Triggered Control](https://mlsanity.com/paper/1809.05152/) (2018)
- [Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control](https://mlsanity.com/paper/1902.01459/) (2019)
- [Machine Learning in Event-Triggered Control: Recent Advances and Open Issues](https://mlsanity.com/paper/2009.12783/) (2020)
- [Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning](https://mlsanity.com/paper/2011.13365/) (2020)
- [Event-triggered Pulse Control with Model Learning (if Necessary)](https://mlsanity.com/paper/1903.08046/) (2019)
- [Computationally efficient stochastic MPC: a probabilistic scaling approach](https://mlsanity.com/paper/2005.10572/) (2020)

## Built on by (most cited first, 7 of 7)

- [HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving](https://mlsanity.com/paper/2505.15793/) (2025)
- [Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.13481/) (2024)
- [Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies](https://mlsanity.com/paper/2506.18304/) (2025)
- [MIND-Stack: Modular, Interpretable, End-to-End Differentiability for Autonomous Navigation](https://mlsanity.com/paper/2505.21734/) (2025)
- [Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning](https://mlsanity.com/paper/2604.13891/) (2026)
- [Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning](https://mlsanity.com/paper/2605.10482/) (2026)
- [Deep Reinforcement Learning-Enhanced Event-Triggered Data-Driven Predictive Control for a 3D Cable-Driven Soft Robotic Arm](https://mlsanity.com/paper/2606.26048/) (2026)

## 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.