7 citations · 21 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal
In this paper, we consider an infinite horizon average reward Markov Decision Process (MDP). Distinguishing itself from existing works within this context, our approach harnesses t…
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…
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach
Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal +2
Reinforcement learning is widely used in applications where one needs to perform sequential decisions while interacting with the environment. The problem becomes more challenging w…
Concave Utility Reinforcement Learning with Zero-Constraint Violations
Mridul Agarwal, Qinbo Bai, Vaneet Aggarwal
We consider the problem of tabular infinite horizon concave utility reinforcement learning (CURL) with convex constraints. For this, we propose a model-based learning algorithm tha…
Markov Decision Processes with Long-Term Average Constraints
Mridul Agarwal, Qinbo Bai, Vaneet Aggarwal
We consider the problem of constrained Markov Decision Process (CMDP) where an agent interacts with a unichain Markov Decision Process. At every interaction, the agent obtains a re…
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…
Model-Free Algorithm and Regret Analysis for MDPs with Long-Term Constraints
Qinbo Bai, Vaneet Aggarwal, Ather Gattami
In the optimization of dynamical systems, the variables typically have constraints. Such problems can be modeled as a constrained Markov Decision Process (CMDP). This paper conside…