Off-road navigation is a challenging problem both at the planning level to
get a smooth trajectory and at the control level to avoid flipping over,
hitting obstacles, or getting stuck at a rough patch. There have been several
recent works using classical approaches involving depth map prediction followed
by smooth trajectory planning and using a controller to track it. We design an
end-to-end reinforcement learning (RL) system for an autonomous vehicle in
off-road environments using a custom-designed simulator in the Unity game
engine. We warm-start the agent by imitating a rule-based controller and
utilize Proximal Policy Optimization (PPO) to improve the policy based on a
reward that incorporates Control Barrier Functions (CBF), facilitating the
agent's ability to generalize effectively to real-world scenarios. The training
involves agents concurrently undergoing domain-randomized trials in various
environments. We also propose a novel simulation environment to replicate
off-road driving scenarios and deploy our proposed approach on a real buggy RC
car.
Videos and additional results: this https URL

使用自定义的模拟器和强化学习系统，在 Unity 游戏引擎中为自主车辆设计了一个端到端的深度强化学习解决方案，通过基于奖励的训练和控制栏函数，帮助智能体在真实场景中进行泛化，并通过提出的模拟环境在实际 RC 汽车上部署该方法。