Ponce, HiramHiramPonce2023-07-302023-07-30201797833196242739783319624280https://scripta.up.edu.mx/handle/20.500.12552/442410.1007/978-3-319-62428-0_18Reinforcement 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. © Springer International Publishing AG 2017.enArtificial organic networksMachine learningRegularizationReinforcement learning Artificial intelligenceCarbonDecision makingHydrocarbonsLearning algorithmsLearning systemsSoft computingCurse of dimensionalityResource Types::text::book::book part