FCMAC: a fuzzified cerebellar model articulation controller with self-organizing capacity
Automatica (Journal of IFAC)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces
Automatic basis function construction for approximate dynamic programming and reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Classifier prediction based on tile coding
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Analysis of an evolutionary reinforcement learning method in a multiagent domain
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Samuel meets Amarel: automating value function approximation using global state space analysis
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Function approximation via tile coding: automating parameter choice
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Robust and fast learning for fuzzy cerebellar model articulation controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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Most of reinforcement learning (RL) algorithms use value function to seek the optimal policy. In large or even continuous states, function approximation approaches must be used to represent value function. The structures of function approximators influence the learning performance greatly. However, the design of structures relies too much on human designer and inappropriate design can lead to poor performance. In this paper, we propose a novel function approximator called Fuzzy CMAC (FCMAC) with automatic state partition (ASP-FCMAC) to automate the structure design for FCMAC. Based on CMAC (also known as tile coding), ASP-FCMAC employs fuzzy membership function to lower the computation load, and analyzes Bellman error as well as learning weights to partition the state automatically so as to generate the structure of FCMAC. Empirical results in both mountain car and RoboCup Keepaway domains demonstrate that ASP-FCMAC can automatically generate the structure of FCMAC and agent using it can learn efficiently.