Deep Q-Networks (DQNs) have emerged as a transformative approach in the realm of autonomous UAV swarm control, particularly for waypoint determination and adherence. At their core, DQNs combine the strengths of Q-learning—a reinforcement learning technique—with deep neural networks to enable agents to learn optimal actions in complex, high-dimensional environments. This fusion allows UAVs to make intelligent decisions based on raw sensory inputs, such as position, velocity, and environmental cues, without requiring handcrafted rules or exhaustive programming.

In the context of UAV swarms, waypoint determination refers to the process of selecting a sequence of spatial coordinates that each drone must follow to achieve mission objectives—be it surveillance, search and rescue, or environmental monitoring. Traditional methods for waypoint planning often rely on centralized control systems or pre-defined trajectories, which can be rigid and vulnerable to dynamic changes in the environment. DQNs, however, offer a decentralized and adaptive alternative. Each UAV can independently learn to navigate toward waypoints while considering the positions and behaviors of its neighbors, obstacles, and mission constraints.

One of the key advantages of DQNs in swarm coordination is their ability to model the waypoint planning problem as a Markov Decision Process (MDP). In this framework, each UAV observes its current state (e.g., location, heading, proximity to obstacles), selects an action (e.g., move to a neighboring grid cell), and receives a reward based on the outcome (e.g., proximity to target, collision avoidance). Over time, the DQN learns a policy that maximizes cumulative rewards, effectively guiding the UAV through optimal waypoints. This approach has been successfully applied in multi-agent scenarios where drones must maintain formation while navigating complex terrains.

For example, Xiuxia et al. proposed a DQN-based method for multi-UAV formation transformation, where the swarm adapts its configuration from an initial to a target formation by learning optimal routes for each drone. The system models the transformation as an MDP and uses DQN to determine the best movement strategy for each UAV, ensuring collision-free transitions and minimal energy expenditure. Similarly, Yilan et al. implemented a DQN-driven waypoint planning system that divides the 3D environment into grids. Each UAV selects its next move based on DQN predictions, optimizing path efficiency and obstacle avoidance.

To enhance learning efficiency, modern DQN implementations often incorporate techniques like prioritized experience replay and target networks. Prioritized experience replay allows UAVs to learn more effectively by focusing on experiences with high temporal difference errors—those that offer the most learning value. Target networks stabilize training by decoupling the Q-value updates from the current network predictions, reducing oscillations and improving convergence.

Moreover, DQNs support scalability and robustness in swarm operations. Because each UAV learns independently using local observations and shared policies, the system can accommodate large swarms without overwhelming communication channels or computational resources. This decentralized learning paradigm also enhances fault tolerance; if one UAV fails or deviates, others can adapt without compromising the entire mission.

In real-world deployments, DQN-based swarm control has shown promise in dynamic environments such as urban landscapes, disaster zones, and contested airspaces. By continuously learning from interactions, UAVs can adjust their waypoint strategies in response to changing conditions, such as wind patterns, moving obstacles, or evolving mission goals.

There is speculation that self-organizing maps (SOM) can be integrated with DQN where UAV swarm must optimize formation under environmental constraints. SOMs can preprocess high-dimensional state spaces into simplified input for Q-network, cluster environmental features such as terrain obstacles, traffic density, to guide UAVs towards optimal formations, improves exploration efficiencies by identifying promising regions in the state-action space. When combined with Multi-agent Reinforcement Learning (MARL) for decentralized decision-making and GNN for modeling inter-agent relationships and spatial topology, MARL-SOM-GNNs architecture enables a UAV swarm to dynamically adapt formation based on clustered environmental features, maximize flow and coverage in constrained environments and maintain robust co-ordination even with partial observability or noisy data.

Finally, Deep Q-Networks offer a powerful, flexible, and scalable solution for UAV swarm waypoint determination and adherence. By enabling autonomous learning and decision-making, DQNs pave the way for intelligent aerial systems capable of executing complex missions with minimal human intervention.