3 accepted papers
Reinforcement learning (RL) has shown promising performance in autonomous driving applications in recent years. The early end-to-end RL method is usually unexplainable and fails to generate stable actions, while the hierarchical RL (HRL) method can tackle the above issues by dividing complex problem…
In this paper, we present a novel learnable and continuous Monte-Carlo Tree Search method, named as KB-Tree, for motion planning in autonomous driving. The proposed method utilizes an asymptotical PUCB based on Kernel Regression (KR-AUCB) as a novel UCB variant, to improve the exploitation and explo…
Reinforcement learning agents need exploratory behaviors to escape from local optima. These behaviors may include both immediate dithering perturbation and temporally consistent exploration. To achieve these, a stochastic policy model that is inherently consistent through a period of time is in desi…