Towards Fast Rates for Federated and Multi-Task Reinforcement Learning

Understand

We consider a setting involving $N$ agents, where each agent interacts with an environment modeled as a Markov Decision Process (MDP).

• The agents' MDPs differ in their reward functions, capturing heterogeneous objectives/tasks.
• The collective goal of the agents is to communicate intermittently via a central server to find a policy that maximizes the average of long-term cumulative rewards across environments.
• The limited existing work on this topic either only provide asymptotic rates, or generate biased policies, or fail to establish any benefits of collaboration.