Online Planning Algorithms for POMDPs

Area of Science:

• Artificial Intelligence
• Reinforcement Learning
• Robotics

Background:

• Partially Observable Markov Decision Processes (POMDPs) are essential for sequential decision-making in uncertain environments.
• Solving POMDPs is computationally challenging for large-scale problems.

Purpose of the Study:

• To survey and analyze existing online POMDP methods.
• To evaluate the performance of online approaches in diverse environments.
• To identify efficient methods for handling large POMDP domains.

Main Methods:

• Focus on online algorithms that compute local policies at each decision step.
• Employ lookahead search within online algorithms.
• Evaluate methods using metrics such as return, error bound reduction, and lower bound improvement.

Main Results:

• Online heuristic search methods demonstrate efficiency in handling large POMDP domains.
• Experimental results validate the effectiveness of state-of-the-art online approaches.
• The study provides a comprehensive analysis of online POMDP algorithms.

Conclusions:

• Online heuristic search is a viable and efficient strategy for solving complex POMDPs.
• These methods offer practical solutions for real-world sequential decision-making under uncertainty.
• Further research can build upon these findings for advanced POMDP solvers.