  Reinforcement Learning (RL) has recently found wide applications in network
traffic management and control because some of its variants do not require
prior knowledge of network models. In this paper, we present a novel scheduler
for real-time multimedia delivery in multipath systems based on an Actor-Critic
(AC) RL algorithm. We focus on a challenging scenario of real-time video
streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths.
The scheduler acting as an RL agent learns in real-time the optimal policy for
path selection, path rate allocation and redundancy estimation for flow
protection. The scheduler, implemented as a module of the GStreamer framework,
can be used in real or simulated settings. The simulation results show that our
scheduler can target a very low loss rate at the receiver by dynamically
adapting in real-time the scheduling policy to the path conditions without
performing training or relying on prior knowledge of network channel models.
