Federated Reinforcement Learning Explained for Beginners

Federated Reinforcement Learning combines two exciting concepts: reinforcement learning and federated learning.

Reinforcement learning means that a computer learns to make decisions by learning from rewards and punishments.

Federated learning allows multiple computers to learn together without sharing their data.

In Federated Reinforcement Learning, many devices or agents work together to learn better without exchanging private data.

This way, intelligent systems can be trained securely and privacy-friendly in various fields, for example in robots, autonomous vehicles, or smart home devices.