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Delayed Reinforcement, Fuzzy Q-Learning and Fuzzy Logic Controllers (1996)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bonarini, Andrea |
| Description | . In this paper, we discuss situations arising with reinforcement learning algorithms, when the reinforcement is delayed. The decision to consider delayed reinforcement is typical in many applications, and we discuss some motivations for it. Then, we summarize Q-Learning, a popular algorithm to deal with delayed reinforcement, and its recent extensions to use it to learn fuzzy logic structures (Fuzzy Q-Learning). Moreover, we present how a reinforcement learning algorithm we have developed in the past (ELF - Evolutionary Learning of Fuzzy rules) implements an extension of the popular Q-Learning algorithm for the distribution of delayed reinforcement when the controller to be learnt is a Fuzzy Logic Controller (FLC). Finally, we present some examples of the application of ELF to learning FLCs that implement behaviors for an autonomous agent. Keywords. Fuzzy Logic Controllers, Q-Learning, Reinforcement Learning. 1. Introduction The main goal of the research presented in this paper i... |
| File Format | |
| Language | English |
| Publisher | Springer Verlag |
| Publisher Date | 1996-01-01 |
| Access Restriction | Open |
| Subject Keyword | Elf Evolutionary Learning Delayed Reinforcement Fuzzy Logic Structure Fuzzy Q-learning Popular Algorithm Reinforcement Learning Recent Extension Fuzzy Logic Controller Many Application Autonomous Agent Main Goal Popular Q-learning Algorithm Fuzzy Rule Implement Behavior |
| Content Type | Text |
| Resource Type | Article |
