Autonomous Control and Path Planning of UAV with Deep Reinforcement Learning

Autonomous unmanned aerial vehicles (UAV) can be applied as a substitution for many manual processes, which results in solutions that are more cost-optimal and less prone to human error. In this paper, we consider a task that requires a quadrotor UAV to reach waypoints from an environment as fast as possible. This work presents various reinforcement learning experiments on the autonomous control and path planning task while exploring the potential of current state-of-the-art algorithms, including the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which addresses the problem of overestimation of value estimates and suboptimal policies commonly present in continuous control domain actor-critic models. The experiments also include different optimization techniques for finding the best set of hyperparameters. We evaluate the trained reinforcement learning agents and provide a detailed comparison and discussion of the results.