Constructing States for Reinforcement Learning
POMDPs are the models of choice for reinforcement learning (RL) tasks where the environment cannot be observed directly. In many applications we need to learn the POMDP structure and parameters from experience and this is considered to be a difficult problem. In this paper we address this issue by modeling the hidden environment with a novel class of models that are less expressive, but easier to learn and plan with than POMDPs. We call these models deterministic Markov models (DMMs), which are...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of International Conference on Machine Learning (ICML 2010)|
|01_Mahmud_Constructing_States_for_2010.pdf||385.68 kB||Adobe PDF||Request a copy|
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