Reinforcement Learning

An essential function of multi-view camera systems is novel view synthesis (NVS), which attempts to generate photorealistic images from new perspectives using source photos. The subfields of human NVS have the potential to significantly contribute to...
Amazon Web Services (AWS) has introduced a new update to its secure data-sharing service, Clean Rooms, enhancing its capabilities with cutting-edge machine learning (ML) and differential privacy features. These advancements empower enterprises to collaborate securely, harness...

Revolutionizing Digital Art: Researchers at Seoul National University Introduce a Novel Approach to Collage Creation Using Reinforcement Learning

Artistic collage creation, a field deeply intertwined with human artistry, has sparked interest in artificial intelligence (AI). The challenge arises from the need to...

This AI Paper Introduces Φ-SO: A Physical Symbolic Optimization Framework that Uses Deep Reinforcement Learning to Discover Physical Laws from Data

Artificial Intelligence and Deep learning have brought about some great advancements in the field of technology. They are enabling robots to perform activities that...

Duke University Researchers Propose Policy Stitching: A Novel AI Framework that Facilitates Robot Transfer Learning for Novel Combinations of Robots and Tasks

In robotics, researchers face challenges in using reinforcement learning (RL) to teach robots new skills, as these skills can be sensitive to changes in...

Google Research Explores: Can AI Feedback Replace Human Input for Effective Reinforcement Learning in Large Language Models?

Human feedback is essential to improve and optimize machine learning models. In recent years, reinforcement learning from human feedback (RLHF) has proven extremely effective...

DeepMind Researchers Introduce Reinforced Self-Training (ReST): A Simple algorithm for Aligning LLMs with Human Preferences Inspired by Growing Batch Reinforcement Learning (RL)

Large language models (LLMs) are outstanding at producing well-written content and resolving various linguistic problems. These models are trained using vast volumes of text...

DeepMind Researchers Introduce AlphaStar Unplugged: A Leap Forward in Large-Scale Offline Reinforcement Learning by Mastering the Real-Time Strategy Game StarCraft II

Games have long served as crucial testing grounds for evaluating the capabilities of artificial intelligence (AI) systems. As AI technologies have evolved, researchers have...

Stanford Researchers Explore Emergence of Simple Language Skills in Meta-Reinforcement Learning Agents Without Direct Supervision: Unpacking the Breakthrough in a Customized Multi-Task Environment

A research team from Stanford University has made groundbreaking progress in the field of Natural Language Processing (NLP) by investigating whether Reinforcement Learning (RL)...

UC Berkeley Researchers Introduce Video Prediction Rewards (VIPER): An Algorithm That Leverages Pretrained Video Prediction Models As Action-Free Reward Signals For Reinforcement Learning

Designing a reward function by hand is time-consuming and can result in unintended consequences. This is a major roadblock in developing reinforcement learning (RL)-based...

Meet MACTA: An Open-Sourced Multi-Agent Reinforcement Learning Approach for Cache Timing Attacks and Detection

We are deluged with multiple forms of data. Be it data from a financial sector, healthcare, educational sector, or an organization. Privacy and security...

5 Reasons Why Large Language Models (LLMs) Like ChatGPT Use Reinforcement Learning Instead of Supervised Learning for Finetuning

With the huge success of Generative Artificial Intelligence in the past few months, Large Language Models are continuously advancing and improving. These models are...

Do You Really Need Reinforcement Learning (RL) in RLHF? A New Stanford Research Proposes DPO (Direct Preference Optimization): A Simple Training Paradigm For Training...

When trained on massive datasets, huge unsupervised LMs acquire powers that surprise even their creators. These models, however, are trained on information produced by...

A New Deep Reinforcement Learning (DRL) Framework can React to Attackers in a Simulated Environment and Block 95% of Cyberattacks Before They Escalate

Cybersecurity defenders must dynamically adapt their techniques and tactics as technology develops and the level of complexity in a system surges. As machine learning...

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