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Action 1 Automatic state construction 1 Decision tree 1 Information gain ratio test 1 Kolmogorov-Smirnov two sample test 1 Partial observability 1 Policy 1 Policy search method 1 Reinforcement learning 1 Reward 1 State 1 U-Tree 1 Value based method 1
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Automatic State Construction using DecisionTrees for Reinforcement Learning Agents
Au, Manix - 2005
Reinforcement Learning (RL) is a learning framework in which an agent learns a policy from continual interaction with the environment. A policy is a mapping from states to actions. The agent receives rewards as feedback on the actions performed. The objective of RL is to design autonomous agents...
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