Reinforcement Learning

Researchers from South Korea Propose a Machine Learning Model that Adjusts Video Game Difficulty based on Player Emotions

Dynamic difficulty adjustment (DDA) is a technique for automatically altering a game's features, behaviors, and scenarios in real-time based on the player's proficiency so...

In Latest Machine Learning Research, A Group at CMU Release a Simple and Efficient Implementation of Recurrent Model-Free Reinforcement Learning (RL) for Future Work...

Most real-world situations involve noise and incomplete information, unlike decision-making algorithms, which often concentrate on simple problems where most information is already available. To...

Researchers At Seoul National University Developed A Deep Learning Framework To Improve A Robotic Sketching Agent’s Skills

This article's primary research objective was to develop something cool with non-rule-based techniques such as deep learning; they believed drawing is cool to display...

Amazon AI Introduces ‘PAVE’: A Novel Reinforcement Learning Model That Uses Lazy-MDP Formalism To Improve Recall of Product Attribute Extraction Models

Millions of products are available in e-commerce stores' catalogs. A significant portion of these products is listed by independent vendors. There are often errors...

Researchers at The University of Luxembourg Develop a Method to Learn Grasping Objects on the Moon from 3D Octree Observations with Deep Reinforcement Learning

The goal of planetary exploration is to improve science by revealing new information about the geology and resource potential of other worlds. Extraterrestrial robotic...

Researchers From Princeton And Max Planck Developed A Reinforcement Learning–Based Simulation That Shows The Human Desire Always To Want More May Have Evolved As...

Through the means of a computational framework of reinforcement learning, researchers from Princeton University have tried to find the relationship between happiness with habituation...

Researchers from DeepMind and University College London Propose Stochastic MuZero for Stochastic Model Learning

Recent research has shown that model-based reinforcement learning is incredibly effective. However, learning a model separately from using it during planning can be challenging...

Nvidia AI Research Team Presents A Deep Reinforcement Learning (RL) Based Approach To Create Smaller And Faster Circuits

There is a law known as Moore's law, which states that the number of transistors on a microchip doubles every two years. And as...

UC Berkeley and Google AI Researchers Introduce ‘Director’: a Reinforcement Learning Agent that Learns Hierarchical Behaviors from Pixels by Planning in the Latent Space...

UC Berkeley and Google AI Researchers Introduce 'Director': a Reinforcement Learning Agent that Learns Hierarchical Behaviors from Pixels by Planning in the Latent Space...

Deepmind AI Researchers Introduce ‘DeepNash’, An Autonomous Agent Trained With Model-Free Multiagent Reinforcement Learning That Learns To Play The Game Of Stratego At Expert...

For several years, the Stratego board game has been regarded as one of the most promising areas of research in Artificial Intelligence. Stratego is...

Researchers From the Berlin Institute of Technology Introduce a New Model based on Deep Reinforcement Learning That Could Allow Mobile Robots to Follow and Guide...

With the ever-changing technology, we are witnessing advancements in technologies every day. One such advancement has come in the Assistance robots. These are primarily...

UC Berkeley Researchers Use a Dreamer World Model to Train a Variety of Real-World Robots to Learn from Experience

Robots need to learn from experience to solve complex in real-world environments. Deep reinforcement learning has been the most common approach to robot learning...

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