Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning
This addresses path planning for multi-agent systems like drones, but it is incremental as it builds on existing reinforcement learning and LSTM methods.
The paper tackled multi-agent path planning for varying numbers of agents by using reinforcement learning with a Long Short-Term Memory module to encode states, achieving collision-free autonomous navigation in real-world flight tests with up to four drones.
Reinforcement learning-based path planning for multi-agent systems of varying size constitutes a research topic with increasing significance as progress in domains such as urban air mobility and autonomous aerial vehicles continues. Reinforcement learning with continuous state and action spaces is used to train a policy network that accommodates desirable path planning behaviors and can be used for time-critical applications. A Long Short-Term Memory module is proposed to encode an unspecified number of states for a varying, indefinite number of agents. The described training strategies and policy architecture lead to a guidance that scales to an infinite number of agents and unlimited physical dimensions, although training takes place at a smaller scale. The guidance is implemented on a low-cost, off-the-shelf onboard computer. The feasibility of the proposed approach is validated by presenting flight test results of up to four drones, autonomously navigating collision-free in a real-world environment.