Extending the Peak Bandwidth of Parameters for Softmax Selection in Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Softmax selection is a widely used action selection method in reinforcement learning.
• Complex methods often require extensive parameter tuning, increasing implementation difficulty.
• Simpler methods like softmax offer implementation and tuning cost savings.

Purpose of the Study:

• To improve the parameter setting of softmax selection.
• To extend the range of effective parameters (bandwidth) for softmax selection.
• To reduce the cost of implementation and parameter tuning in reinforcement learning.

Main Methods:

• Leveraging the asymptotic equipartition property of Markov decision processes.
• Developing an enhanced parameter setting for softmax selection.
• Evaluating performance across various episodic tasks.

Main Results:

• The proposed setting effectively extends the parameter bandwidth for softmax selection.
• The enhanced method demonstrates improved policy stability.
• Quantitative assessment via statistical tests confirms bandwidth extension.

Conclusions:

• The improved softmax selection parameter setting offers a practical advantage.
• This approach balances effectiveness with reduced implementation and tuning complexity.
• The method shows promise for more stable and cost-efficient reinforcement learning applications.