Dynamic-depth context tree weighting
Reinforcement learning (RL) in partially observable settings is challenging be- cause the agent’s immediate observations are not Markov. Recently proposed methods can learn variable-order Markov models of the underlying process but have steep memory requirements and are sensitive to aliasing betw...
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| 格式: | Conference item |
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Curran Associates
2018
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