Abstract Point-based value iteration methods are a kind of algorithms for effectively solving partially observable Markov decision process (POMDP) model. However, the algorithm efficiency is limited by the belief point set explored in most of the algorithms by single heuristic criterion. A hybrid heuristic value iteration algorithm (HHVI) for exploring belief point set is presented in this paper. The upper and lower bounds on the value function are maintained and only the belief points with its value function bounds difference greater than the threshold are selected to expand. Furthermore, the furthest belief point away from the explored point set among the subsequent belief points with the above difference also greater than the threshold is explored. The convergence effect of HHVI is guaranteed by making the explored point set fully distributed in the reachable belief space. Experimental results of four benchmarks show that HHVI can guarantee the convergence efficiency and obtain better global optimal solution.
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2016, Vol. 29 
