S. Koenig. Exploring Unknown Environments with Real-Time Search or Reinforcement Learning. In Advances in Neural Information Processing Systems (NIPS), pages 1003-1009, 1999.

Abstract: Learning Real-Time A* (LRTA*) is a popular control method that interleaves planning and plan execution and has been shown to solve search problems in known environments efficiently. In this paper, we apply LRTA* to the problem of getting to a given goal location in an initially unknown environment. Uninformed LRTA* with maximal lookahead always moves on a shortest path to the closest unvisited state, that is, to the closest potential goal state. This was believed to be a good exploration heuristic, but we show that it does not minimize the worst-case plan-execution time compared to other uninformed exploration methods. This result is also of interest to reinforcement-learning researchers since many reinforcement learning methods use asynchronous dynamic programming, interleave planning and plan execution, and exhibit optimism in the face of uncertainty, just like LRTA*.

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