摘要
AI researchers typically formulated probabilistic planning under uncertainty problems using Markov Decision Processes (MDPs).Value Iteration is an inef?cient algorithm for MDPs, because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Among them, LAO*, LRTDP and HDP are state-of-the-art ones. All of these use reach ability analysis and heuristics to avoid some unnecessary backups. However, none of these approaches fully exploit the graphical features of the MDPs or use these features to yield the best backup sequence of the state space. We introduce an improved algorithm named Topological Order Value Iteration (TOVI) that can circumvent the problem of unnecessary backups by detecting the structure of MDPs and backing up states based on topological sequences. The experimental results demonstrate the effectiveness and excellent performance of our algorithm.
AI researchers typically formulated probabilistic planning under uncertainty problems using Markov Decision Processes (MDPs).Value Iteration is an inef?cient algorithm for MDPs, because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Among them, LAO*, LRTDP and HDP are state-of-the-art ones. All of these use reach ability analysis and heuristics to avoid some unnecessary backups. However, none of these approaches fully exploit the graphical features of the MDPs or use these features to yield the best backup sequence of the state space. We introduce an improved algorithm named Topological Order Value Iteration (TOVI) that can circumvent the problem of unnecessary backups by detecting the structure of MDPs and backing up states based on topological sequences. The experimental results demonstrate the effectiveness and excellent performance of our algorithm.
