In planning with partially observable Markov decision processes, pre-compiled policies are often represented as finite state controllers or sets of alpha-vectors, which provide a lower bound on the value of the optimal policy. Some algorithms (e.g., HSVI2, SARSOP, GapMin) also compute an upper bound to guide the search and to offer performance guarantees, but they do not derive a policy from this upper bound due to computational reasons. The execution of a policy derived from an upper bound requires a one step lookahead simulation to determine the next best action and the evaluation of the upper bound at the reachable beliefs is complicated and costly (i.e., linear programming or sawtoooth approximation). The first aim of this paper is to show principled and computationally cheap ways of executing upper bound policies which can be even faster than executing lower bound policies based on alpha vectors. The second complementary contribution is a new method to find better upper bound policies that outperforms those obtained by existing algorithms, such as HSVI2, SARSOP, or GapMin, on a suite of benchmarks. Our approach is based on a novel synthesis of augmented and deterministic POMDPs and it facilitates efficient optimization of upper bound policies.
['Marek Grzes', 'Pascal Poupart']
POMDP planning and execution in an augmented space