  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.
