Bias Variance

یکی از کاربردهای رو به افزایش یادگیری عمیق تقویتی استفاده در سیستم های self driving car است. در مقاله ای که چند روز پیش ارایه شد یک راه حل end-to-end ارایه شده است که در مورد آن می خوانیم:

— Autonomous driving in urban crowds at unregulated intersections is challenging, where dynamic occlusions
and uncertain behaviors of other vehicles should be carefully
considered. Traditional methods are heuristic and based on
hand-engineered rules and parameters, but scale poorly in new
situations. Therefore, they require high labor cost to design
and maintain rules in all foreseeable scenarios. Recently, deep
reinforcement learning (DRL) has shown promising results
in urban driving scenarios. However, DRL is known to be
sample inefficient, and most previous works assume perfect
observations such as ground-truth locations and motions of
vehicles without considering noises and occlusions, which might
be a too strong assumption for policy deployment. In this work,
we use DRL to train lidar-based end-to-end driving policies that
naturally consider imperfect partial observations. We further
use unsupervised contrastive representation learning as an
auxiliary task to improve the sample efficiency. The comparative
evaluation results reveal that our method achieves higher success rates than the state-of-the-art (SOTA) lidar-based end-toend driving network, better trades off safety and efficiency than
the carefully-tuned rule-based method, and generalizes better
to new scenarios than the baselines. Demo videos are available
at https://caipeide.github.io/carl-lead/.


لینک مقاله



#معرفی_مقاله #یادگیری_عمیق #یادگیری_عمیق_تقویتی #self_driving_car

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caipeide.site

Peide Cai | Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning

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171 viewsBias Variance, edited  Sep 27, 2021 at 19:11

یکی از کاربردهای رو به افزایش یادگیری عمیق تقویتی استفاده در سیستم های self driving car است. در مقاله ای که چند روز پیش ارایه شد یک راه حل end-to-end ارایه شده است که در مورد آن می خوانیم:

— Autonomous driving in urban crowds at unregulated intersections is challenging, where dynamic occlusions
and uncertain behaviors of other vehicles should be carefully
considered. Traditional methods are heuristic and based on
hand-engineered rules and parameters, but scale poorly in new
situations. Therefore, they require high labor cost to design
and maintain rules in all foreseeable scenarios. Recently, deep
reinforcement learning (DRL) has shown promising results
in urban driving scenarios. However, DRL is known to be
sample inefficient, and most previous works assume perfect
observations such as ground-truth locations and motions of
vehicles without considering noises and occlusions, which might
be a too strong assumption for policy deployment. In this work,
we use DRL to train lidar-based end-to-end driving policies that
naturally consider imperfect partial observations. We further
use unsupervised contrastive representation learning as an
auxiliary task to improve the sample efficiency. The comparative
evaluation results reveal that our method achieves higher success rates than the state-of-the-art (SOTA) lidar-based end-toend driving network, better trades off safety and efficiency than
the carefully-tuned rule-based method, and generalizes better
to new scenarios than the baselines. Demo videos are available
at https://caipeide.github.io/carl-lead/.


لینک مقاله



#معرفی_مقاله #یادگیری_عمیق #یادگیری_عمیق_تقویتی #self_driving_car

🌴 سایت | 🌺 کانال | 🌳 پشتیبانی

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