Time-Varying Formation Controllers for Unmanned Aerial Vehicles Using Deep Reinforcement Learning
This addresses the need for efficient multi-agent control in UAV applications, but it appears incremental as it applies an existing method to a specific domain.
The paper tackled the problem of designing scalable and portable controllers for UAVs to achieve time-varying formations quickly, confirming that deep reinforcement learning can drive UAVs to any formation while considering optimality and portability.
We consider the problem of designing scalable and portable controllers for unmanned aerial vehicles (UAVs) to reach time-varying formations as quickly as possible. This brief confirms that deep reinforcement learning can be used in a multi-agent fashion to drive UAVs to reach any formation while taking into account optimality and portability. We use a deep neural network to estimate how good a state is, so the agent can choose actions accordingly. The system is tested with different non-high-dimensional sensory inputs without any change in the neural network architecture, algorithm or hyperparameters, just with additional training.