Reinforcement Learning is at the core of building and improving frontier AI models and products. Yet most state-of-the-art RL methods learn primarily from outcomes: a scalar reward signal that says ...

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 ...

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 ...

How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a ...

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 ...

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 ...

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 ...