… To overcome this limitation, we formulate the lane change behavior with continuous action
… Policy Gradient (DDPG). The reward function, which is critical for learning the optimal policy, …

… policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane
changing … This section presents the differences in the lane-changing behaviors of agents under …

… Different reinforcement learning methods can also be applied to continuous action space
(eg Deep Deterministic Policy Gradient) to improve performance. To better exploit the …

… However, improper lane change behaviors … policies than TRPO. Meanwhile, when compared
to value-based methods, PPO is able to compute actions directly from the policy gradient, …

… learn an automated lane change behavior such that it can intelligently make a lane change
under diverse … There are policy gradient based algorithms, eg actorcritic, to directly learn the …

… policy gradient algorithm for autonomous driving to control the continuous actions of vehicles.
For this purpose, we implemented a road … method that can control continuous behavior. …

… lane change behaviors with a comparison of state-of-the-art deep reinforcement learning
algorithms… We develop our algorithm based on Deep Deterministic Policy Gradient (DDPG) [14]…

… model, the actor is used to select the driving action, and the critic mainly implements the
evaluation of the lane-changing driving behavior. In the model, the parameters of the actor and …

… its Recurrent variant called Recurrent Deterministic Policy Gradients. Our trained agent …
change lanes in order to overtake when the traffic is sparse. The reasons for the above behavior…

… and imposes additional requirements for the merging and diverging behavior of vehicles.
For future research, we focus on the extension of the algorithm to cover such cases in a more …