  Optimizing a partially observable Markov decision process (POMDP) policy is
challenging. The policy graph improvement (PGI) algorithm for POMDPs represents
the policy as a fixed size policy graph and improves the policy monotonically.
Due to the fixed policy size, computation time for each improvement iteration
is known in advance. Moreover, the method allows for compact understandable
policies. This report describes the technical details of the PGI [1] and
particle based PGI [2] algorithms for POMDPs in a more accessible way than [1]
or [2] allowing practitioners and students to understand and implement the
algorithms.
