Federated Reinforcement Learning (FRL) is a method where multiple agents in distributed environments improve their policies through reinforcement learning without centralizing their local data.
Each agent learns locally from its own experiences and only shares model parameters or updates with a central server or among each other.
The federated setup enables maintaining privacy and security while aggregating knowledge across different environments.
Typical challenges include data and environment heterogeneity, communication costs, and merging different learning progress.
FRL is applied in areas such as distributed robotic systems, smart grids, or personalized recommendation systems that require dynamic decision-making processes.