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Comparative Analysis of Value Iteration and Policy Iteration in Robotic Decision-Making
- 1 University College London, 25 Gordon Street, London WC1H 0AY, UK
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Abstract
This study focuses on Markov Decision Processes (MDPs) as a framework for decision-making in robotics. MDPs are crucial for enabling autonomous systems to operate in dynamic environments. MDPs enable the development of optimal strategies that balance immediate and future rewards, making them essential for intelligent robotic behavior. The purpose of this study is to evaluate the performance differences of value iteration and strategy iteration algorithms in different robot tasks. The study highlights the strengths and weaknesses of each algorithm. This comparative analysis uniquely addresses a gap in existing research by evaluating both Value Iteration and Policy Iteration side by side, offering critical insights into their respective performances across diverse robotic tasks. Results indicate that Policy Iteration converges faster and adapts better in complex environments, making it ideal for real-time applications, while Value Iteration is more efficient in smaller state spaces.These findings provide critical insights for selecting algorithms tailored to specific robotic applications, highlighting their role in advancing intelligent robotic systems.
Keywords
Markov Decision Process, Value Iteration, Policy Iteration
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Cite this article
Qiu,Y. (2024). Comparative Analysis of Value Iteration and Policy Iteration in Robotic Decision-Making. Applied and Computational Engineering,83,140-147.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MLA 2024 Workshop: Semantic Communication Based Complexity Scalable Image Transmission System for Resource Constrained Devices
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