Reinforcement learning (RL) is an agent-based approach for teaching robots to
navigate within the physical world. Gathering data for RL is known to be a
laborious task, and real-world experiments can be risky. Simulators facilitate
the collection of training data in a quicker and more cost-effective manner.
However, RL frequently requires a significant number of simulation steps for an
agent to become skilful at simple tasks. This is a prevalent issue within the
field of RL-based visual quadrotor navigation where state dimensions are
typically very large and dynamic models are complex. Furthermore, rendering
images and obtaining physical properties of the agent can be computationally
expensive. To solve this, we present a simulation framework, built on AirSim,
which provides efficient parallel training. Building on this framework, Ape-X
is modified to incorporate decentralised training of AirSim environments to
make use of numerous networked computers. Through experiments we were able to
achieve a reduction in training time from 3.9 hours to 11 minutes using the
aforementioned framework and a total of 74 agents and two networked computers.
Further details including a github repo and videos about our project,
PRL4AirSim, can be found at this https URL

该研究提出了一种基于 AirSim 的仿真框架，实现了有效的并行训练、分散式训练和大规模代理训练，从而在机器人的视觉四轴导航中实现了学习时间从 3.9 小时降低到 11 分钟的优化。