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Order-Optimal Global Convergence for Average Reward Reinforcement Learning via Actor-Critic Approach
This work analyzes average-reward reinforcement learning with general parametrization.
Variance-Reduced Policy Gradient Approaches for Infinite Horizon Average Reward Markov Decision Processes
The second approach, rooted in Hessian-based techniques, ensures an expected regret of the order $\tilde{\mathcal{O}}(\sqrt{T})$.
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories.
Online Learning with Adversaries: A Differential-Inclusion Analysis
Our main result is that the proposed algorithm almost surely converges to the desired mean $\mu.$ This makes ours the first asynchronous FL method to have an a. s. convergence guarantee in the presence of adversaries.
Does Momentum Help? A Sample Complexity Analysis
Stochastic Heavy Ball (SHB) and Nesterov's Accelerated Stochastic Gradient (ASG) are popular momentum methods in stochastic optimization.
