In many real-world reinforcement learning (RL) problems, besides optimizing the main
objective function, an agent must concurrently avoid violating a number of constraints. In …

In recent years, reinforcement learning systems with general goals beyond a cumulative
sum of rewards have gained traction, such as in constrained problems, exploration, and …

Trust region policy optimization (TRPO) is a popular and empirically successful policy
search algorithm in Reinforcement Learning (RL) in which a surrogate problem, that restricts …

Policy gradient (PG) methods have been one of the most essential ingredients of
reinforcement learning, with application in a variety of domains. In spite of the empirical …

In this paper, we revisit and improve the convergence of policy gradient (PG), natural PG
(NPG) methods, and their variance-reduced variants, under general smooth policy …

Abstract This paper considers Safe Policy Improvement (SPI) in Batch Reinforcement
Learning (Batch RL): from a fixed dataset and without direct access to the true environment …

In this paper, we propose a novel reinforcement-learning algorithm consisting in a stochastic
variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs) …

Machine learning models, particularly reinforcement learning (RL), have demonstrated great
potential in optimizing video streaming applications. However, the state-of-the-art solutions …

Improving the sample efficiency in reinforcement learning has been a long-standing
research problem. In this work, we aim to reduce the sample complexity of existing policy …

We revisit the stochastic variance-reduced policy gradient (SVRPG) method proposed
by\citet {papini2018stochastic} for reinforcement learning. We provide an improved …