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Basis Function Construction in Reinforcement Learning using CascadeCorrelation Learning Architecture
"... In reinforcement learning, it is a common practice to map the state(action) space to a different one using basis functions. This transformation aims to represent the input data in a more informative form that facilitates and improves subsequent steps. As a “good ” set of basis functions result in b ..."
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In reinforcement learning, it is a common practice to map the state(action) space to a different one using basis functions. This transformation aims to represent the input data in a more informative form that facilitates and improves subsequent steps. As a “good ” set of basis functions result in better solutions and defining such functions becomes a challenge with increasing problem complexity, it is beneficial to be able to generate them automatically. In this paper, we propose a new approach based on Bellman residual for constructing basis functions using cascadecorrelation learning architecture. We show how this approach can be applied to Least Squares Policy Iteration algorithm in order to obtain a better approximation of the value function, and consequently improve the performance of the resulting policies. We also present the effectiveness of the method empirically on some benchmark problems. 1.
ProjectTeam SequeL Sequential Learning
"... c t i v it y e p o r t 2008 Table of contents ..."
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Author manuscript, published in "International Conference on Machine Learning and Applications (2008) 7582" Basis Function Construction in Reinforcement Learning using CascadeCorrelation Learning Architecture
"... A Markov decision process (MDP) is defined by a tuple (S, A, P, R, γ) where S is a set of states, A is a set of actions, P(s, a, s ′ ) is the transition function which dehal00826054, ..."
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A Markov decision process (MDP) is defined by a tuple (S, A, P, R, γ) where S is a set of states, A is a set of actions, P(s, a, s ′ ) is the transition function which dehal00826054,