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 ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

Smart Maze solver Using Reinforcement Learning (RL) aims to develop an agent capable of solving a maze-environment by using its learning in an RL algorithm specifically, Q-learning Algorithm a typical ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

A 3D autonomous drone simulation with AI-powered flight capabilities using deep reinforcement learning. Features realistic physics, LiDAR obstacle detection, and a neural network that learns to ...

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...

Abstract: Smart Maze solver Using Reinforcement Learning (RL) aims to develop an agent capable of solving a maze-environment by using its learning in an RL algorithm specifically, Q-learning Algorithm ...

Ever since DeepSeek burst onto the scene in January, momentum has grown around open source Chinese artificial intelligence models. Some researchers are pushing for an even more open approach to ...