| 摘要 |
An apparatus and method for automatic learning of high-level navigation in partially observable environments with landmarks uses full state information available at the landmark positions to determine navigation policy. Landmark Markov Decision Processes (MDPs) can be generated only for encountered parts of an environment when navigating from a starting state to a goal state within the environment, thereby reducing computational resources needed for a navigation solution that uses a fully modeled environment. An MDP policy is calculated using the SarsaLandmark algorithm, and the policy is transformed to a navigation solution based on the current position and connectivity information.
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