"Federated Reinforcement Learning: Statistical and Communication Trade-offs

Reinforcement learning (RL), concerning decision making in uncertain environments, lies at the heart of modern artificial intelligence. Due to the high dimensionality, training of RL agents typically requires a significant amount of computation and data to achieve desirable performance. However, data collection can be extremely time-consuming with limited access in real-world applications, especially when performed by a single agent. On the other hand, it is plausible to leverage multiple agents to collect data simultaneously, under the premise that they can learn a global policy collaboratively without the need of sharing local data in a federated manner. This talk addresses the fundamental statistical and communication trade-offs in the algorithmic designs of federated RL algorithms, covering both blessings and curses in the presence of data and task heterogeneities across the agents.