论文标题
基于图像的调节，用于通过强化学习的自主微型赛车中的行动政策平滑度
Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning
论文作者
论文摘要
近年来，深度强化学习在低级控制任务方面取得了重大成果。但是，控制平滑性问题的关注较少。在自动驾驶中，不稳定的控制是不可避免的，因为车辆可能会突然改变其动作。此问题将降低控制系统的效率，引起过度的机械磨损，并对车辆造成无法控制的危险行为。在本文中，我们将条件应用于基于图像的输入的行动策略平滑度（CAP），以平滑对自主微型赛车的控制。应用盖子和模拟传输方法有助于以更高的速度稳定控制。尤其是，具有帽子和自行车的特工减少了平均完成单圈时间的21.80％。此外，我们还进行了广泛的实验，以分析CAPS组件的影响。
In recent years, deep reinforcement learning has achieved significant results in low-level controlling tasks. However, the problem of control smoothness has less attention. In autonomous driving, unstable control is inevitable since the vehicle might suddenly change its actions. This problem will lower the controlling system's efficiency, induces excessive mechanical wear, and causes uncontrollable, dangerous behavior to the vehicle. In this paper, we apply the Conditioning for Action Policy Smoothness (CAPS) with image-based input to smooth the control of an autonomous miniature car racing. Applying CAPS and sim-to-real transfer methods helps to stabilize the control at a higher speed. Especially, the agent with CAPS and CycleGAN reduces 21.80% of the average finishing lap time. Moreover, we also conduct extensive experiments to analyze the impact of CAPS components.
