RLinf is a flexible and scalable open-source RL infrastructure designed for Embodied and Agentic AI. The 'inf' in RLinf stands for Infrastructure, highlighting its role as a robust backbone for ...

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 reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Abstract: This paper presents a deep reinforcement learning (RL) approach for training mobile robots to navigate complex environments using the Twin Delayed Deep Deterministic Policy Gradient (TD3) ...

AgiBot announced a key milestone this week with the successful deployment of its Real-World Reinforcement Learning system in a manufacturing pilot with Longcheer Technology. The pilot project marks ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

The authors present a biologically plausible framework for action selection and learning in the striatum that is a fundamental advance in our understanding of possible neural implementations of ...

What if the very techniques we rely on to make AI smarter are actually holding it back? A new study has sent shockwaves through the AI community by challenging the long-held belief that reinforcement ...

Forbes contributors publish independent expert analyses and insights. Author, Researcher and Speaker on Technology and Business Innovation. Apr 19, 2025, 03:24am EDT Apr 21, 2025, 10:40am EDT ...