Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling
observations from the environment is usually split across multiple agents. However …

In this paper, we consider federated Q-learning, which aims to learn an optimal Q-function
by periodically aggregating local Q-estimates trained on local data alone. Focusing on …

We consider the problem of Federated Q-learning, where $ M $ agents aim to collaboratively
learn the optimal Q-function of an unknown infinite horizon Markov Decision Process with …

This paper considers distributed stochastic optimization, in which a number of agents
cooperate to optimize a global objective function through local computations and information …

Actor-critic (AC) algorithms have been widely used in decentralized multi-agent systems to
learn the optimal joint control policy. However, existing decentralized AC algorithms either …

In this paper, we consider federated reinforcement learning for tabular episodic Markov
Decision Processes (MDP) where, under the coordination of a central server, multiple …

Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in
recent years and has found many scientific and engineering applications. However, a key …

In this paper, we consider model-free federated reinforcement learning for tabular episodic
Markov decision processes. Under the coordination of a central server, multiple agents …

Two-timescale stochastic approximation (TTSA) is among the most general frameworks for
iterative stochastic algorithms. This includes well-known stochastic optimization methods …

We provide a new non-asymptotic analysis of distributed temporal difference learning with
linear function approximation. Our approach relies on “one-shot averaging,” where N agents …