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Reduced Rule Base in Fuzzy Rule Interpolation-based Q-learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Vincze, Dávid Kovács, Szilveszter |
| Copyright Year | 2009 |
| Abstract | Fuzzy Q-learning, the fuzzy extension of the Reinforcement Learning (RL) is a well known topic in computational intelligence. It can be used to tackle control problems in unknown continuous environments without defining an exact method on how to solve it explicitly. In the RL concept the problem needed to be solved is hidden in the feedback of the environment, called reward or punishment (positive or negative reward). From these rewards the system can learn which action is considered to be the best choice in a given state. One of the most frequently applied RL method is the “Q-learning”. The goal of the Q-learning method is to find an optimal policy for the system by building the state-actionvalue function. The state-action-value-function is a function of the expected return (a function of the cumulative reinforcements), related to a given state and a taken action following the optimal policy. The original Q-learning method was introduced for discrete states and actions. With the application of fuzzy reasoning the method can be adapted for continuous environment, called Fuzzy Q-learning (FQ-Learning). Traditional Fuzzy Qlearning embeds the 0-order Takagi-Sugeno fuzzy inference and hence inherits the requirement of the state-action-value-function representation to be a complete fuzzy rule base. An extension of the traditional fuzzy Q-learning method with the capability of handling sparse fuzzy rule bases is already introduced by the authors, which suggests a Fuzzy Rule Interpolation (FRI) method, namely the FIVE (Fuzzy rule Interpolation based on Vague Environment) technique to be the reasoning method applied with Q-learning (FRIQ-learning). The main goal of this paper is the introduction of a method which can construct the requested FRI fuzzy model in a reduced size. The suggested reduction is achieved by incremental creation of an intentionally sparse fuzzy rule base. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://uni-obuda.hu/conferences/cinti2009/49_cinti2009_submission.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
