A parameter-free learning automaton scheme.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Traditional learning automaton (LA) schemes require manual parameter tuning for optimal performance in stochastic environments.
• This manual tuning is time-consuming and interaction-costly, posing a significant limitation for LA applications, particularly in expensive interaction settings.
• Existing LA methods struggle with adaptability to unknown or changing stochastic environments due to their reliance on pre-configured parameters.

Purpose of the Study:

• To propose a novel parameter-free learning automaton (PFLA) scheme that obviates the need for manual parameter tuning.
• To demonstrate the efficacy of PFLA in maintaining stable and reliable performance across various stochastic environments without parameter adjustments.
• To reduce the complexity and cost associated with applying learning automata in unknown or dynamic settings.

Main Methods:

• Development of a parameter-free learning automaton (PFLA) scheme.
• Utilizing a Bayesian inference method to enable autonomous parameter configuration.
• Rigorous mathematical proof of ϵ-optimality for the proposed PFLA scheme.

Main Results:

• The proposed PFLA scheme achieves competitive performance comparable to well-tuned traditional LA schemes.
• PFLA demonstrates superior consistency in performance across different environments compared to untuned LA schemes.
• The parameter-free nature of PFLA significantly reduces the difficulty of applying learning automata to unknown stochastic environments.

Conclusions:

• The parameter-free learning automaton (PFLA) effectively eliminates the need for manual parameter tuning.
• PFLA offers a robust and adaptable solution for stochastic environments, reducing application complexity and cost.
• The proposed scheme provides a viable alternative to traditional LA methods, ensuring consistent and competitive performance without parameter tuning.