Incremental state aggregation for value function estimation in reinforcement learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Estimating value functions in reinforcement learning is challenging due to large state and action spaces.
• Basis functions are commonly used but require significant prior knowledge for preparation.
• Existing adaptive basis function methods are computationally expensive.

Purpose of the Study:

• To propose an efficient method for approximating value functions in reinforcement learning.
• To overcome the computational cost associated with adaptive basis function construction.
• To reduce the difficulty of preparing basis functions in reinforcement learning.

Main Methods:

• Decomposing the value function approximation problem into smaller subproblems.
• Solving each subproblem with reduced computational cost.
• Utilizing a linear combination of basis functions for value function representation.

Main Results:

• The proposed method significantly reduces CPU time compared to existing techniques.
• Efficient approximation of value functions is achieved even with large state spaces.
• Subproblem decomposition leads to manageable computational loads.

Conclusions:

• The novel approach offers a computationally efficient solution for value function approximation in reinforcement learning.
• This method addresses the limitations of prior knowledge requirements and high computational costs of existing techniques.
• The decomposition strategy proves effective in optimizing reinforcement learning computations.