5 papers
Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
Yang Xu, Swetha Ganesh, Washim Uddin Mondal +2
This paper investigates infinite-horizon average reward Constrained Markov Decision Processes (CMDPs) with general parametrization. We propose a Primal-Dual Natural Actor-Critic al…
Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm
Qinbo Bai, Mridul Agarwal, Vaneet Aggarwal
Many engineering problems have multiple objectives, and the overall aim is to optimize a non-linear function of these objectives. In this paper, we formulate the problem of maximiz…
Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal
This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDPs). To the best of our knowledge, this work is the first to delve into t…
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
Vaneet Aggarwal, Washim Uddin Mondal, Qinbo Bai
Reinforcement Learning (RL) serves as a versatile framework for sequential decision-making, finding applications across diverse domains such as robotics, autonomous driving, recomm…
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm
Qinbo Bai, Amrit Singh Bedi, Vaneet Aggarwal
We consider the problem of constrained Markov decision process (CMDP) in continuous state-actions spaces where the goal is to maximize the expected cumulative reward subject to som…