# ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates

arXiv: https://arxiv.org/abs/2403.01564 · HTML: https://mlsanity.com/paper/2403.01564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving](https://mlsanity.com/paper/1909.07707/) (2019)
- [Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty](https://mlsanity.com/paper/1504.08053/) (2015)
- [ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1706.03235/) (2017)
- [DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Driving](https://mlsanity.com/paper/2308.15991/) (2023)
- [Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs](https://mlsanity.com/paper/2303.10302/) (2023)
- [Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment](https://mlsanity.com/paper/2312.03263/) (2023)
- [Stochastic 2-D Motion Planning with a POMDP Framework](https://mlsanity.com/paper/1810.00204/) (2018)

## Built on by (most cited first, 4 of 4)

- [GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments](https://mlsanity.com/paper/2410.15178/) (2024)
- [TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning](https://mlsanity.com/paper/2412.02570/) (2024)
- [TRACE: A Self-Improving Framework for Robot Behavior Forecasting with Vision-Language Models](https://mlsanity.com/paper/2503.00761/) (2025)
- [Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing](https://mlsanity.com/paper/2508.12166/) (2025)

## Abstract (arXiv)

Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates -- the process by which the agent obtains its true state information from the sensors -- are limited, presents a significant challenge. Traditional methods often struggle to balance resource conservation, accurate state estimation and precise tracking, resulting in suboptimal performance. This problem is particularly pronounced in environments with large action spaces, where the need for frequent, accurate state data is paramount, yet the capacity for active localization updates is restricted by external limitations. This paper introduces ComTraQ-MPC, a novel framework that combines Deep Q-Networks (DQN) and Model Predictive Control (MPC) to optimize trajectory tracking with constrained active localization updates. The meta-trained DQN ensures adaptive active localization scheduling, while the MPC leverages available state information to improve tracking. The central contribution of this work is their reciprocal interaction: DQN's update decisions inform MPC's control strategy, and MPC's outcomes refine DQN's learning, creating a cohesive, adaptive system. Empirical evaluations in simulated and real-world settings demonstrate that ComTraQ-MPC significantly enhances operational efficiency and accuracy, providing a generalizable and approximately optimal solution for trajectory tracking in complex partially observable environments.