Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAV

This paper presents multi-agent reinforcement learning frameworks for the low-level control of a quadrotor UAV. While single-agent reinforcement learning has been successfully applied to quadrotors, training a single monolithic network is often data-intensive and time-consuming. To address this, we decompose the quadrotor dynamics into the translational dynamics and the yawing dynamics, and assign a reinforcement learning agent to each part for efficient training and performance improvements. The proposed multi-agent framework for quadrotor low-level control that leverages the underlying structures of the quadrotor dynamics is a unique contribution. Further, we introduce regularization terms to mitigate steady-state errors and to avoid aggressive control inputs. Through benchmark studies with sim-to-sim transfer, it is illustrated that the proposed multi-agent reinforcement learning substantially improves the convergence rate of the training and the stability of the controlled dynamics.