The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...

University of Birmingham experts have created open-source computer software that helps scientists understand how fast-moving ...

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 ...

In this tutorial, we build an advanced agentic Deep Reinforcement Learning system that guides an agent to learn not only actions within an environment but also how to choose its own training ...

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 ...

Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem ...

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

Abstract: Q-learning (QL) is a widely used algorithm in reinforcement learning (RL), but its convergence can be slow, especially when the discount factor is close to one. Successive over-relaxation ...