Abstract
Real-time search methods allow an agent to move in unknown environments. We provide two enhancements to the real-time search algorithm HLRTA*(k). First, we give a better way to perform bounded propagation, generating the HLRTA* LS (k) algorithm. Second, we consider the option of doing more than one action per planning step, by analyzing the quality of the heuristic found during lookahead, producing the HLRTA*(k,d) algorithm. We provide experimental evidence of the benefits of both algorithms, with respect to other real-time algorithms on existing benchmarks.KeywordsOptimal PathGoal StateBounded PropagationLocal SpaceShort Path AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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