基于深度强化学习的自主导航中安全约束的评估
Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning
AI总结：
本研究比较了考虑与不考虑安全约束的两种深度强化学习导航策略，证明安全策略能在不牺牲整体性能的前提下生成间隙更大、碰撞更少的轨迹。
摘要：
尽管强化学习算法在自主导航领域取得了巨大成功，但若不考虑安全约束，它们无法直接应用于真实的自主系统。后者对于避免自动驾驶车辆在道路上的不安全行为至关重要。为了突出这些约束的重要性，本研究比较了两种可学习的导航策略：安全策略与不安全策略。安全策略考虑了这些约束，而另一种则没有。我们表明，安全策略能够生成具有更大间隙（与障碍物的距离）的轨迹，并在训练期间减少碰撞，同时不牺牲整体性能。
英文摘要：
While reinforcement learning algorithms have had great success in the field of autonomous navigation, they cannot be straightforwardly applied to the real autonomous systems without considering the safety constraints. The later are crucial to avoid unsafe behaviors of the autonomous vehicle on the road. To highlight the importance of these constraints, in this study, we compare two learnable navigation policies: safe and unsafe. The safe policy takes the constraints into account, while the other does not. We show that the safe policy is able to generate trajectories with more clearance (distance to the obstacles) and makes less collisions while training without sacrificing the overall performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
