Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement Learning

TL;DR

This paper introduces a decentralized deep reinforcement learning framework for multi-robot navigation, enabling cooperative, safe, and efficient movement in complex environments with limited shared information.

Contribution

It proposes a novel decentralized RL approach with shared policy learning for multi-robot navigation under sparse observations and dynamic obstacles.

Findings

Robust training in stochastic environments with asynchronous agents.

Effective navigation with limited state information and continuous actions.

Successful collision avoidance and goal achievement in simulated settings.

Abstract

This work presents a decentralized motion planning framework for addressing the task of multi-robot navigation using deep reinforcement learning. A custom simulator was developed in order to experimentally investigate the navigation problem of 4 cooperative non-holonomic robots sharing limited state information with each other in 3 different settings. The notion of decentralized motion planning with common and shared policy learning was adopted, which allowed robust training and testing of this approach in a stochastic environment since the agents were mutually independent and exhibited asynchronous motion behavior. The task was further aggravated by providing the agents with a sparse observation space and requiring them to generate continuous action commands so as to efficiently, yet safely navigate to their respective goal locations, while avoiding collisions with other dynamic peers and static obstacles at all times. The experimental results are reported in terms of quantitative measures and qualitative remarks for both training and deployment phases.