How agents acquire abstract concepts from sparse, diverse examples—often without explicit supervision—remains a central ...

What happens when companies on digital labor platforms no longer decide for themselves how much to pay their workers, but leave this to learning algorithms? Researchers at TU Darmstadt, Bielefeld ...

In a recent study published in Nature Communications, researchers created a memristor that uses a built-in oxygen gradient to produce slow, stable conductance changes, enabling a reinforcement ...

Most machine learning systems learn from labeled examples. You show them thousands of photos tagged "cat" or "dog," and they figure out the difference. Reinforcement learning takes a fundamentally ...

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine ...

This project implements a Q-learning algorithm to train an AI agent to play the classic Snake game. The agent learns to navigate the game board, collect food, and avoid collisions through ...

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 ...

Humans use diverse skills and strategies to effectively manipulate various objects, ranging from dexterous in-hand manipulation (fine motor skills) to complex whole-body manipulation (gross motor ...

Automated design of quantum circuits presents a significant challenge as researchers strive to unlock the full potential of quantum computing, and a team led by Siddhant Dutta from Nanyang ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...