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Finite time analysis of the pursuit algorithm for learning automata
1Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore.
Summary
This study introduces a new framework for analyzing learning automaton behavior in finite time, crucial for understanding convergence rates. The framework is applied to the Pursuit Algorithm, comparing continuous and discrete versions.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Theoretical Computer Science
Background:
- Analyzing the finite time behavior of learning automata is essential for understanding their convergence rates.
- Current methods may not fully capture the nuances of learning algorithm performance over time.
Purpose of the Study:
- To propose a general framework for the finite time analysis of automaton learning algorithms.
- To apply this framework to the Pursuit Algorithm and compare its continuous and discretized forms.
Main Methods:
- Developed a novel analytical framework for finite time behavior analysis.
- Applied the framework to both continuous and discretized versions of the Pursuit Algorithm.
- Compared the convergence rates of the analyzed algorithms.
Main Results:
- The proposed framework enables detailed finite time analysis of learning automata.
- Quantified and compared the convergence rates of continuous and discretized Pursuit Algorithms.
- Demonstrated the framework's utility in understanding algorithm performance.
Conclusions:
- The new framework provides a robust method for analyzing finite time behavior and convergence rates.
- The analysis offers insights into the trade-offs between continuous and discretized Pursuit Algorithms.
- The framework is a valuable alternative to Probably Approximately Correct (PAC) learning for specific analyses.
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