  With the development of industry, drones are appearing in various field. In
recent years, deep reinforcement learning has made impressive gains in games,
and we are committed to applying deep reinforcement learning algorithms to the
field of robotics, moving reinforcement learning algorithms from game scenarios
to real-world application scenarios. We are inspired by the LunarLander of
OpenAI Gym, we decided to make a bold attempt in the field of reinforcement
learning to control drones. At present, there is still a lack of work applying
reinforcement learning algorithms to robot control, the physical simulation
platform related to robot control is only suitable for the verification of
classical algorithms, and is not suitable for accessing reinforcement learning
algorithms for the training. In this paper, we will face this problem, bridging
the gap between physical simulation platforms and intelligent agent, connecting
intelligent agents to a physical simulation platform, allowing agents to learn
and complete drone flight tasks in a simulator that approximates the real
world. We proposed a reinforcement learning framework based on Gazebo that is a
kind of physical simulation platform (ROS-RL), and used three continuous action
space reinforcement learning algorithms in the framework to dealing with the
problem of autonomous landing of drones. Experiments show the effectiveness of
the algorithm, the task of autonomous landing of drones based on reinforcement
learning achieved full success.
