论文标题
学习开车（L2D）作为现实增强学习的低成本基准
Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning
论文作者
论文摘要
我们提出学习驱动（L2D），这是现实世界增强学习（RL）的低成本基准。 L2D涉及一个简单且可重现的实验设置，其中RL代理必须学会在三个微型轨道周围驾驶驴车，只有单眼图像观测和汽车的速度。代理商必须学习从脱离轨道上驾驶的脱离进程，这是在轨道上驱动的情况下发生的。我们介绍和开放源训练管道，这使得将任何现有的RL算法应用于使用驴车自动驾驶的任务变得直接。我们测试了拟议的L2D基准测试模仿学习，最新模型和基于模型的算法。我们的结果表明，现有的RL算法可以学会在不到五分钟的互动中从头开始驾驶汽车。我们证明，RL算法可以从稀疏和嘈杂的脱离接触中学习，以比模仿学习和人类操作员更快地推动。
We present Learning to Drive (L2D), a low-cost benchmark for real-world reinforcement learning (RL). L2D involves a simple and reproducible experimental setup where an RL agent has to learn to drive a Donkey car around three miniature tracks, given only monocular image observations and speed of the car. The agent has to learn to drive from disengagements, which occurs when it drives off the track. We present and open-source our training pipeline, which makes it straightforward to apply any existing RL algorithm to the task of autonomous driving with a Donkey car. We test imitation learning, state-of-the-art model-free, and model-based algorithms on the proposed L2D benchmark. Our results show that existing RL algorithms can learn to drive the car from scratch in less than five minutes of interaction. We demonstrate that RL algorithms can learn from sparse and noisy disengagement to drive even faster than imitation learning and a human operator.
