Provable policy gradient methods for average-reward markov potential games
We study Markov potential games under the infinite horizon average reward criterion. Most
previous studies have been for discounted rewards. We prove that both algorithms based on …
previous studies have been for discounted rewards. We prove that both algorithms based on …
Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
We study infinite-horizon average-reward Markov decision processes (AMDPs) in the
context of general function approximation. Specifically, we propose a novel algorithmic …
context of general function approximation. Specifically, we propose a novel algorithmic …
Stochastic first-order methods for average-reward markov decision processes
We study average-reward Markov decision processes (AMDPs) and develop novel first-
order methods with strong theoretical guarantees for both policy optimization and policy …
order methods with strong theoretical guarantees for both policy optimization and policy …
Variance-Reduced Policy Gradient Approaches for Infinite Horizon Average Reward Markov Decision Processes
We present two Policy Gradient-based methods with general parameterization in the context
of infinite horizon average reward Markov Decision Processes. The first approach employs …
of infinite horizon average reward Markov Decision Processes. The first approach employs …
Optimistic Q-learning for average reward and episodic reinforcement learning
We present an optimistic Q-learning algorithm for regret minimization in average reward
reinforcement learning under an additional assumption on the underlying MDP that for all …
reinforcement learning under an additional assumption on the underlying MDP that for all …
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
S Vakili, J Olkhovskaya - arXiv preprint arXiv:2410.23498, 2024 - arxiv.org
Reinforcement learning utilizing kernel ridge regression to predict the expected value
function represents a powerful method with great representational capacity. This setting is a …
function represents a powerful method with great representational capacity. This setting is a …
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
Reinforcement Learning (RL) serves as a versatile framework for sequential decision-
making, finding applications across diverse domains such as robotics, autonomous driving …
making, finding applications across diverse domains such as robotics, autonomous driving …
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
This paper proposes a computationally tractable algorithm for learning infinite-horizon
average-reward linear mixture Markov decision processes (MDPs) under the Bellman …
average-reward linear mixture Markov decision processes (MDPs) under the Bellman …
Provably Efficient Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs
We resolve the open problem of designing a computationally efficient algorithm for infinite-
horizon average-reward linear Markov Decision Processes (MDPs) with $\widetilde {O}(\sqrt …
horizon average-reward linear Markov Decision Processes (MDPs) with $\widetilde {O}(\sqrt …
Concentration of Cumulative Reward in Markov Decision Processes
In this paper, we investigate the concentration properties of cumulative rewards in Markov
Decision Processes (MDPs), focusing on both asymptotic and non-asymptotic settings. We …
Decision Processes (MDPs), focusing on both asymptotic and non-asymptotic settings. We …