Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Organizing Gaussian Fuzzy CMAC with Truth Value Restriction
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Reinforcement distribution in fuzzy Q-learning
Fuzzy Sets and Systems
Fuzzy CMAC with automatic state partition for reinforcementlearning
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Fuzzy epoch-incremental reinforcement learning algorithm
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
| Hi-index | 0.00 |
The adaptation of temporal differences method TD(λ0) to reinforcement learning algorithms with fuzzy approximation of action-value function is proposed. Eligibility traces are updated using the normalized degree of activation of fuzzy rules. The two types of fuzzy reinforcement learning algorithm are formulated: with discrete and with continuous action values. These new algorithms are practically tested in the control of two typical models of continuous object, like ball-beam and cart-pole system. The achievement results are compared with two popular reinforcement learning algorithms with CMAC and table approximation of action-value function.