Abstract: Reinforcement learning (RL) is a promising approach for end-to-end autonomous driving, but its practical deployment remains challenging due to low sample efficiency and sensitivity to reward ...

Abstract: Reinforcement learning (RL) has emerged as an effective system for managing nonlinear robotic systems, where classical control methods often encounter instability, delayed convergence, and ...

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...

I built a simple 2D platformer game and then implemented a Q-learning reinforcement learning algorithm that taught an agent how to win that game. More details can be found in report Upon opening the ...

Before diving into the details, let’s look at a high-level overview outlining vocabulary terms we’ll see come up and contrasting different methods. It would also be useful to revisit this section ...

Accurately estimating the Q-function is a central challenge in offline reinforcement learning. However, existing approaches often rely on a single global Q-function, which struggles to capture the ...

This model applies SARSA reinforcement learning for efficient urban traffic and pedestrian management, incorporating simulation, algorithmic implementation, and evaluation to enhance safety and reduce ...

To provide quantitative analysis of strategic confrontation game such as cross-border trades like tariff disputes and competitive scenarios like auction bidding, we propose an alternating Markov ...