Autonomous Navigation of Swarms in 3D Environments Using Deep Reinforcement Learning
The presence of swarm intelligence in many natural systems has always been an inspiration to develop such distributed intelligence in artificial multi agent systems. It finds its applications in high level control of complex swarms, distributed sensing technologies, and telecom networks. In this paper, we present an end to end approach to train a group of cooperative agents to navigate through 3 dimensional (3D) environments. The problem is particularly hard because the agents can only observe the environment partially and the number of agents in the swarm (also known as the size of the swarm) may change over time. Our approach uses deep reinforcement learning, mapping raw sensory data to high level commands, in order to optimize (1) navigation, and (2) distributed assembly of the swarm while keeping the swarm (3) unaffected from its size dynamics. Here, we use suitable reward shaping for navigation and distributed assembly and deal size dynamics by exploiting histograms as an observational input to the model. The simulations were performed in the Unity 3D engine. The results demonstrate that our approach effectively improves swarm navigation and assembly in rough 3D environments and can be generalized to real world scenarios.
2020 International Symposium on Recent Advances in Electrical Engineering Computer Sciences (RAEE CS)