Relevance determination in reinforcement learning
نویسندگان
چکیده
We propose relevance determination and minimisation schemes in reinforcement learning which are solely based on the Q-matrix and which can thus be applied during training without prior knowledge about the system dynamics. On the one hand, we judge the relevance of separate state space dimensions based on the variance in the Q-matrix. On the other hand, we perform Q-matrix reduction by means of a combination of Qlearning with neighbourhood cooperation of the state values where the neighbourhood is defined based on the Q-values itself. The effectivity of the methods is shown in a (simple though relevant) gridworld example.
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