Online Planning Algorithms for POMDPs
This work provides a comparative analysis for researchers in AI and robotics dealing with uncertainty, but it is incremental as it reviews existing methods rather than introducing new ones.
The paper surveys and evaluates online planning algorithms for POMDPs to address computational intractability in sequential decision-making under uncertainty, finding that state-of-the-art heuristic search methods can efficiently handle large domains.
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that state-of-the-art online heuristic search methods can handle large POMDP domains efficiently.