A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes

@inproceedings{KearnsAT1999ASS,
  title={A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes},
  author={ProcessesMichael KearnsAT},
  year={1999}
}
  • ProcessesMichael KearnsAT
  • Published 1999
An issue that is critical for the application of Markov decision processes (MDPs) to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas-tic environments with very large or even inn-nite state spaces, traditional planning and reinforcement learning algorithms are often inappli-cable, since their running time typically scales linearly with the state space size in the worst case. In this paper we present a new algorithm that, given only a generative… CONTINUE READING
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