@sambodhi 2018-07-05T09:29:45.000000Z 字数 3908 阅读 795

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Google Chrome # Learning to drive in a day.

### 我们的方法的潜在影响是巨大的。

Today’s self-driving cars are stuck at good but not good enough performance levels. Here, we have provided evidence for the first viable framework to quickly improving driving algorithms from being mediocre to being roadworthy. The ability to quickly learn to solve tasks through clever trial and error is what has made humans incredibly versatile machines capable of evolution and survival. We learn through a mixture of imitation, and lots of trial and error for everything from riding a bicycle, to learning how to cook.

DeepMind have shown us that deep reinforcement learning methods can lead to super-human performance in many games including Go, Chess and computer games, almost always outperforming any rule based system. We here show that a similar philosophy is also possible in the real world, and in particular, in autonomous vehicles. A crucial point to note is that DeepMind’s Atari playing algorithms required millions of trials to solve a task. It is remarkable that we consistently learnt to lane-follow in under 20 trials.
DeepMind已经向我们展示了深度强化学习方法可以在许多游戏中导致超人的表现，包括围棋、象棋和电脑游戏，几乎总是比任何基于规则的系统表现得更好。我们在这里展示了一种类似的哲学在现实世界中也是可能的，特别是在自动驾驶汽车中。需要注意的一点是，DeepMind的Atari的算法需要数百万次试验才能解决一个任务。值得注意的是，我们在不到20次的试验中始终学会了“走单行道”。

### Imagine what we could learn to do in a day…?

Wayve has a philosophy that to build robotic intelligence we do not need massive models, fancy sensors and endless data. What we need is a clever training process that learns rapidly and efficiently, like in our video above. Hand-engineered approaches to the self-driving problem have reached an unsatisfactory glass ceiling in performance. Wayve is attempting to unlock autonomous driving capabilities with smarter machine learning.
Wayve的理念是，要构建机器人智能，我们不需要庞大的模型、花哨的传感器和无穷无尽的数据。我们需要的是一个聪明的训练过程，快速有效地学习，就像我们上面的视频。人工设计的自动驾驶技术在性能上达到了令人不满意的玻璃天花板。Wayve正试图用更智能的机器学习来开发自动驾驶功能。

Special thanks: We would like to thank StreetDrone for building us an awesome robotic vehicle, Admiral for insuring our vehicle trials and the Cambridge Polo Clubfor granting us access to their private land for our lane-following research.

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