Improving POMDP tractability via belief compression and clustering.

Area of Science:

• Artificial Intelligence
• Robotics
• Operations Research

Background:

• Partially Observable Markov Decision Processes (POMDPs) are crucial for planning in uncertain environments.
• The computational intractability of POMDPs stems from high-dimensional belief spaces, hindering large-scale problem-solving.
• Existing methods struggle with the curse of dimensionality in POMDP belief spaces.

Purpose of the Study:

• To develop a hybrid approach for reducing POMDP belief space dimensionality.
• To improve the efficiency of computing optimal policies for large-scale POMDPs.
• To maintain the quality of policies while reducing computational cost.

Main Methods:

• Integration of belief compression and value-directed compression techniques.
• Novel orthogonal nonnegative matrix factorization for belief compression.
• K-means-like clustering to partition the belief space into sub-POMDPs for further dimension reduction.

Main Results:

• Demonstrated effectiveness of the proposed belief compression and clustering approaches on benchmark problems.
• Significant reduction in the cost of computing POMDP policies.
• Preservation of policy quality despite dimensionality reduction.

Conclusions:

• The hybrid approach offers a scalable solution for POMDP planning problems.
• Belief space partitioning and dimension reduction are effective strategies for improving computational efficiency.
• The method retains policy optimality, making it suitable for real-world applications.