a sarsa(λ) algorithm based on double-layer fuzzy reasoning

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ID: 137287
2013
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Abstract
Solving reinforcement learning problems in continuous space with function approximation is currently a research hotspot of machine learning. When dealing with the continuous space problems, the classic Q-iteration algorithms based on lookup table or function approximation converge slowly and are difficult to derive a continuous policy. To overcome the above weaknesses, we propose an algorithm named DFR-Sarsa(λ) based on double-layer fuzzy reasoning and prove its convergence. In this algorithm, the first reasoning layer uses fuzzy sets of state to compute continuous actions; the second reasoning layer uses fuzzy sets of action to compute the components of Q-value. Then, these two fuzzy layers are combined to compute the Q-value function of continuous action space. Besides, this algorithm utilizes the membership degrees of activation rules in the two fuzzy reasoning layers to update the eligibility traces. Applying DFR-Sarsa(λ) to the Mountain Car and Cart-pole Balancing problems, experimental results show that the algorithm not only can be used to get a continuous action policy, but also has a better convergence performance.
Reference Key
liu2013mathematicala1 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Quan Liu;Xiang Mu;Wei Huang;Qiming Fu;Yonggang Zhang
Journal journal of power sources
Year 2013
DOI
10.1155/2013/561026
URL
http://dx.doi.org/10.1155/2013/561026
Keywords
civil engineering (general)mathematics engineering (general)

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