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- W2740042657 abstract "Reinforcement learning aims to solve the problem of learning optimal or near-optimal decision-making policies for a given domain problem. However, it is known that increasing the dimensionality of the input space (i.e. environment) will increase the complexity for the learning algorithms, falling into the curse of dimensionality. Value function approximation and hierarchical reinforcement learning have been two different approaches proposed to alleviate reinforcement learning from this illness. In that sense, this paper proposes a new value function approximation using artificial hydrocarbon networks –a supervised learning method inspired on chemical carbon networks– with regularization at each subtask in a hierarchical reinforcement learning framework. Comparative results using a greedy sparse value function approximation over the MAXQ hierarchical method was computed, proving that artificial hydrocarbon networks improves accuracy and efficiency on the value function approximation." @default.
- W2740042657 created "2017-08-08" @default.
- W2740042657 creator A5016698194 @default.
- W2740042657 date "2017-01-01" @default.
- W2740042657 modified "2023-09-23" @default.
- W2740042657 title "A Novel Artificial Hydrocarbon Networks Based Value Function Approximation in Hierarchical Reinforcement Learning" @default.
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- W2740042657 doi "https://doi.org/10.1007/978-3-319-62428-0_18" @default.
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