Stochastic calculus-guided reinforcement learning: A probabilistic framework for optimal decision-making.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Decision Theory

Background:

• Decision-making in uncertain environments is challenging.
• Traditional Stochastic Reinforcement Learning (SRL) has limitations in complex scenarios.

Purpose of the Study:

• Introduce Stochastic Calculus-guided Reinforcement Learning (SCRL) as an advanced decision-making framework.
• Compare the performance and risk profile of SCRL against traditional SRL methods.

Main Methods:

• Developed SCRL by integrating principles of stochastic calculus into reinforcement learning.
• Conducted empirical tests to evaluate SCRL's adaptability, performance, and risk compared to SRL.
• Assessed metrics including training rewards, learning progress, and rolling averages.

Main Results:

• SCRL demonstrated superior performance over SRL across various metrics.
• SCRL achieved a lower dispersion value (63.49) than SRL (65.96).
• SCRL exhibited significantly lower short-term (0.64) and long-term (0.78) risk values compared to SRL (18.64 and 10.41, respectively).

Conclusions:

• SCRL offers a more robust and less risky approach to decision-making in uncertain and complex situations.
• The findings highlight SCRL's potential for real-world applications requiring careful decision-making.
• SCRL represents a significant advancement over traditional SRL methods.