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A new class of epsilon-optimal learning automata
Georgios I Papadimitriou1, Maria Sklira, Andreas S Pomportsis
1Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece. gp@csd.auth.gr
Summary
A novel stochastic estimator enhances learning automata performance in random environments. This new approach ensures faster, more accurate convergence to optimal actions compared to existing methods.
Area of Science:
- Machine Learning
- Artificial Intelligence
- Control Theory
Background:
- Learning automata are computational models that learn to select optimal actions in an environment.
- Traditional deterministic estimators can be slow to adapt, especially in stationary random environments.
- P-model absorbing learning automata are a specific class used for decision-making under uncertainty.
Purpose of the Study:
- To introduce a new class of P-model absorbing learning automata utilizing a stochastic estimator.
- To improve the speed and accuracy of convergence to optimal actions in stationary random environments.
- To analyze the asymptotic behavior and prove the epsilon-optimality of the proposed scheme.
Main Methods:
- Development of a novel stochastic estimator for learning automata.
- Relaxation of dependence on environmental responses for infrequently selected actions.
- Analysis of asymptotic behavior and theoretical proof of epsilon-optimality.
- Extensive simulations comparing the proposed scheme with deterministic methods (DP(RI) and DGPA).
Main Results:
- The proposed stochastic estimator allows infrequently chosen actions a better chance to be identified as optimal.
- The learning automaton demonstrates rapid and accurate convergence to the optimal action.
- The scheme is theoretically proven to be epsilon-optimal in all stationary random environments.
- Simulation results show faster convergence compared to deterministic-estimator-based schemes.
Conclusions:
- The introduced stochastic estimator offers a significant advancement for P-model absorbing learning automata.
- The new approach enhances learning efficiency and reliability in stationary random environments.
- The findings suggest a more robust and faster learning mechanism for adaptive systems.
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