Parallel reinforcement learning for weighted multi-criteria model with adaptive margin.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Control Theory

Background:

• Reinforcement learning (RL) is applied to a linear family of tasks.
• Optimal policies can be nonlinear even within linear task families.
• Naive approaches fail to find optimal policies due to this nonlinearity.

Purpose of the Study:

• To address the challenge of nonlinear optimal policies in linear task families.
• To overcome the problem of exploding set sizes in existing simultaneous Q-learning algorithms.
• To introduce a novel method for efficient reinforcement learning in linear task families.

Main Methods:

• The study focuses on reinforcement learning (RL) within a linear task family.
• It highlights the nonlinearity of optimal solutions as a key challenge.
• Adaptive margins are introduced to manage computational complexity.

Main Results:

• A method is presented to calculate results equivalent to Q-learning for multiple tasks simultaneously.
• The proposed adaptive margins effectively overcome the issue of exploding set sizes.
• This facilitates the computation of optimal policies in previously intractable scenarios.

Conclusions:

• Reinforcement learning in linear task families requires handling nonlinear optimal policies.
• Adaptive margins provide an effective solution to the computational challenges posed by existing algorithms.
• The introduced method enhances the feasibility of applying RL to complex linear task structures.