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...
| Главные авторы: | , |
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| Формат: | Conference item |
| Опубликовано: |
Curran Associates
2018
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