  Since reinforcement learning algorithms are notoriously data-intensive, the
task of sampling observations from the environment is usually split across
multiple agents. However, transferring these observations from the agents to a
central location can be prohibitively expensive in terms of the communication
cost, and it can also compromise the privacy of each agent's local behavior
policy. In this paper, we consider a federated reinforcement learning framework
where multiple agents collaboratively learn a global model, without sharing
their individual data and policies. Each agent maintains a local copy of the
model and updates it using locally sampled data. Although having N agents
enables the sampling of N times more data, it is not clear if it leads to
proportional convergence speedup. We propose federated versions of on-policy
TD, off-policy TD and Q-learning, and analyze their convergence. For all these
algorithms, to the best of our knowledge, we are the first to consider
Markovian noise and multiple local updates, and prove a linear convergence
speedup with respect to the number of agents. To obtain these results, we show
that federated TD and Q-learning are special cases of a general framework for
federated stochastic approximation with Markovian noise, and we leverage this
framework to provide a unified convergence analysis that applies to all the
algorithms.
