What are the common learning algorithms used by AI Agents?

Common learning algorithms used by AI agents include supervised learning, unsupervised learning, reinforcement learning, and deep learning.

1. Supervised Learning: The agent learns from labeled data, where the input and corresponding correct output are provided. It maps inputs to outputs by minimizing errors.

   • Example: Predicting house prices based on features like size and location. The model is trained on historical data with known prices.
   • Relevant Service: Tencent Cloud TI-ONE (Tencent Intelligent Optimization Engine) supports supervised learning workflows.

2. Unsupervised Learning: The agent finds patterns in unlabeled data without predefined outputs. It groups or clusters data based on similarities.

   • Example: Customer segmentation in marketing by clustering users with similar behaviors.
   • Relevant Service: Tencent Cloud EMR (Elastic MapReduce) can process large datasets for unsupervised learning tasks.

3. Reinforcement Learning (RL): The agent learns by interacting with an environment, receiving rewards or penalties for actions, and optimizing its policy to maximize cumulative rewards.

   • Example: Training a robot to navigate a maze by rewarding it for reaching the goal.
   • Relevant Service: Tencent Cloud TI-RL (Reinforcement Learning Platform) provides tools for RL model development.

4. Deep Learning: A subset of machine learning using neural networks with multiple layers to model complex patterns. It excels in tasks like image recognition and NLP.

   • Example: Image classification using convolutional neural networks (CNNs).
   • Relevant Service: Tencent Cloud TI-Platform offers pre-trained deep learning models and GPU-accelerated computing.

These algorithms enable AI agents to adapt, make decisions, and improve performance over time.