DRL, which fuses the strengths of deep learning and reinforcement learning, is increasingly used to support the core ...

Deep reinforcement learning is comparable to supervised machine learning. The model generates actions, and based on the feedback from the environment, it adjusts its parameters. However ...

Deep learning and artificial intelligence ... Five of the most promising emerging trends in this area include federated learning, GANs, XAI, reinforcement learning and transfer learning.

As next-generation mobile networks grow more complex, high-density 5G environments are placing enormous pressure on co ...

Digital systems are expected to navigate real-world environments, understand multimedia content, and make high-stakes ...

Model instability: RL models, especially when used with neural networks (as in deep reinforcement learning), can be unstable during training, requiring careful tuning of hyperparameters to avoid ...

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...

Learn More. Deep learning is a subset of machine learning that uses neural networks with multiple layers to model complicated patterns and representations in data. It excels at tasks like image ...

Deep Learning, and Foundation Models. If you’ve ever wondered how these terms are related, and what sets them apart, you’ll be pleased to know that we’re about to demystify it all.

From machine learning and deep learning to generative AI and natural language processing, different types of AI models serve various use cases—for example, automating tasks, developing better ...