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Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
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Reinforcement Learning (RL) serves as a versatile framework for sequential decision-making, finding applications across diverse domains such as robotics, autonomous driving, recommendation systems, supply chain optimization, biology, mechanics, and finance. The primary objective of these applications is to maximize the average reward. Real-world scenarios often necessitate adherence to specific constraints during the learning process.
This monograph focuses on the exploration of various model-based and model-free approaches for Constrained RL within the context of average reward Markov Decision Processes (MDPs). The investigation commences with an examination of model-based strategies, delving into two foundational methods - optimism in the face of uncertainty and posterior sampling. Subsequently, the discussion transitions to parametrized model-free approaches, where the primal dual policy gradient-based algorithm is explored as a solution for constrained MDPs.
The monograph provides regret guarantees and analyzes constraint violation for each of the discussed setups. For the above exploration, the authors assume the underlying MDP to be ergodic. Further, this monograph extends its discussion to encompass results tailored for weakly communicating MDPs, thereby broadening the scope of its findings and their relevance to a wider range of practical scenarios.
Author: Vaneet Aggarwal, Washim Uddin Mondal, Qinbo Bai
Binding Type: Paperback
Publisher: Now Publishers
Published: 08/21/2024
Series: Foundations and Trends(r) in Optimization
Pages: 116
Weight: 0.39lbs
Size: 9.21h x 6.14w x 0.25d
ISBN: 9781638283966
This monograph focuses on the exploration of various model-based and model-free approaches for Constrained RL within the context of average reward Markov Decision Processes (MDPs). The investigation commences with an examination of model-based strategies, delving into two foundational methods - optimism in the face of uncertainty and posterior sampling. Subsequently, the discussion transitions to parametrized model-free approaches, where the primal dual policy gradient-based algorithm is explored as a solution for constrained MDPs.
The monograph provides regret guarantees and analyzes constraint violation for each of the discussed setups. For the above exploration, the authors assume the underlying MDP to be ergodic. Further, this monograph extends its discussion to encompass results tailored for weakly communicating MDPs, thereby broadening the scope of its findings and their relevance to a wider range of practical scenarios.
Author: Vaneet Aggarwal, Washim Uddin Mondal, Qinbo Bai
Binding Type: Paperback
Publisher: Now Publishers
Published: 08/21/2024
Series: Foundations and Trends(r) in Optimization
Pages: 116
Weight: 0.39lbs
Size: 9.21h x 6.14w x 0.25d
ISBN: 9781638283966
