Decentralized Reinforcement Learning for Multi-Agent Navigation in Unconstrained Environments

Supervised learning has demonstrated to be an effective strategy in training neural networks for vehicle navigation. However, it requires labeled data, which may not be available when a large number of vehicles need to be controlled simultaneously. In contrast, Deep Reinforcement Learning (DRL) circumvents the necessity for ground truth labels through environmental exploration. However, most concurrent DRL approaches either tend to operate in the discrete action/state space or do not consider the vehicle kinematics. In this paper, we use DRL to control multiple vehicles while also considering their kinematics. The task is for all the vehicles to reach their desired destination/target while avoiding collisions with each other or static obstacles in an unconstrained environment. For this, we propose a decentralized Proximal Policy Optimization (PPO) based DRL agent that independently provides control commands to each vehicle. The agent is based on two separate PPO models. The first is used to drive each vehicle to the proximity of its target. Once within the target's proximity, the second model is used to park that vehicle at the correct position and orientation. The decentralized nature of the algorithm allows each agent to rely only on information about its current state and target, along with details regarding the closest obstacle/agent. By scaling this approach to all vehicles, simultaneous navigation of multiple vehicles can be achieved. Experimental results show a collective strategy that allows consistent results across a wide range of scenarios while scaling to situations with up to 20 vehicles and 12 stationary obstacles.