Vision-based reinforcement learning for lane-tracking control

Abstract

<p class="Abstract">The present study focused on vision-based end-to-end reinforcement learning in relation to<strong> </strong>vehicle control problems such as lane following and collision avoidance. The controller policy presented in this paper is able to control a small-scale robot to follow the right-hand lane of a real two-lane road, although its training has only been carried out in a simulation. This model, realised by a simple, convolutional network, relies on images of a forward-facing monocular camera and generates continuous actions that directly control the vehicle. To train this policy, proximal policy optimization was used, and to achieve the generalisation capability required for real performance, domain randomisation was used. A thorough analysis of the trained policy was conducted by measuring multiple performance metrics and comparing these to baselines that rely on other methods. To assess the quality of the simulation-to-reality transfer learning process and the performance of the controller in the real world, simple metrics were measured on a real track and compared with results from a matching simulation. Further analysis was carried out by visualising salient object maps.</p>

Hedging Interest Rate Options with Reinforcement Learning: an investigation of a heavy-tailed distribution

“…Reinforcement learning (RL) is a subfield of machine learning that focuses on training agents to make sequential decisions in an environment to maximize a reward signal (Kaelbling et al, 1996) or a cumulative reward. It has gained significant attention in recent years due to its ability to solve complex decision-making problems in various domains, such as autonomous driving (Sallab et al, 2017), lane-tracking control (Kalapos et al, 2021), and traffic signal control (Chu et al, 2020).…”