  This paper proposes an accelerated method for approximately solving partially
observable Markov decision process (POMDP) problems offline. Our method
carefully combines two existing tools: Anderson acceleration (AA) and the fast
informed bound (FIB) method. Adopting AA, our method rapidly solves an
approximate Bellman equation with an efficient combination of previous solution
estimates. Furthermore, the use of FIB alleviates the scalability issue
inherent in POMDPs. We show the convergence of the overall algorithm to the
suboptimal solution obtained by FIB. We further consider a simulation-based
method and prove that the approximation error is bounded explicitly. The
performance of our algorithm is evaluated on several benchmark problems. The
results of our experiments demonstrate that the proposed algorithm converges
significantly faster without degrading the quality of the solution compared to
its standard counterpart.
