Q-learning　is　a　model-free　iterative　reinforcement　learning　algorithm　that　is　widely　used　for　navigating　mobile　robots　in　unstructured　environments.　However,　the　exploration　and　utilization　of　the　environmental　data　limits　the　Q-learning　convergence　speed　for　mobile　robot　navigation.　This　study　used　the　Q-learning　algorithm　and　the　fact　that　rodents　use　olfactory　cues　for　spatial　orientation　and　navigation　to　develop　a　Q-learning　environmental　cognitive　strategy　based　on　odor-reward　shaping.　This　algorithm　reduces　useless　exploration　of　the　environment　by　improving　the　Q-learning　action　selection　strategy.　Environmental　odor　information　is　integrated　into　the　algorithm　with　the　olfactory　factor　used　to　weight　the　Q-learning　and　the　odor-reward　shaping　in　the　action　selection　strategy.　The　algorithm　effectiveness　is　evaluated　in　a　simulation　of　movement　in　the　labyrinth　environment　used　in　the　Tolman　mouse　experiment.　The　results　show　that　the　Q-learning　algorithm　with　odor-reward　shaping　reduces　useless　exploration　of　the　environment,　enhances　cognitive　learning　of　the　environment,　and　improves　the　algorithm　convergence　speed.　©　2021,　Tsinghua　University　Press.　All　right　reserved.