Can I read Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving on EtoBox?

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving by Shalev-Shwartz, Shai; Shammah, Shaked; Shashua, Amnon is a scholarly article available to read on EtoBox.

What is Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving about?

Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too defensive so that normal traffic flow is maintained. In this paper we apply deep reinforcement learning to the problem of forming long term driving strategies. We note that there are two major challenges that make autonomous driving different from other robotic tasks. First, is the necessity for ensuring functional safety - something that machine learning has difficulty with given that performance is optimized at the level of an expectation over many instances. Second, the Markov Decision Process model often used in robotics is problematic in our case because of unpredictable behavior of other agents in this multi-agent scenario. We make three contributions in our work. First, we show how policy

Author

Shalev-Shwartz, Shai; Shammah, Shaked; Shashua, Amnon

Published

2016

Language

EN