| Title |
Credit-Assigned-CMAC-based Reinforcement Learn ing with Application to the Acrobot Swing Up Control Problem |
| Keywords |
CMAC ; Credit-Assigned ; Function Approximation ; Reinforcement Learning ; Acrobot |
| Abstract |
For real world applications of reinforcement learning techniques, function approximation or generalization will be required to avoid curse of dimensionality. For this, an improved function approximation-based reinforcement teaming method is proposed to speed up convergence by using CA-CMAC(Credit-Assigned Cerebellar Model Articulation Controller). To show that our proposed CACRL(CA-CMAC-based Reinforcement Learning) performs better than the CRL(CMAC- based Reinforcement Learning), computer simulation and experiment results are illustrated, where a swing-up control Problem of an acrobot is considered. |