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When dealing with the continuous space problems,the traditional Q-iteration algorithms based on lookup-table or function approximation converge slowly and are diff lt to get a continuous policy.To overcome the above weak-nesses,an on-policy TD algorithm named DFP-OPTD was proposed based on double-layer fuzzy partitioning and its convergence was proved.The first layer of fuzzy partitioning was applied for state space,the second layer of fuzzy parti-tioning was applied for action space,and Q-value functions were computed by the combination of the two layer fuzzy partitioning.Based on the Q-value function,the consequent parameters of fuzzy rules were updated by gradient descent method.Applying DFP-OPTD on two classical reinforcement learning problems,experimental results show that the algo-rithm not only can be used to get a continuous action policy,but also has a better convergence performance.

When dealing with the continuous space problems,the traditional Q-iteration algorithms based on lookup-table or function approximation converge slowly and are diff lt to get a continuous policy.To overcome the above weak-nesses,an on-policy TD algorithm named DFP-OPTD was proposed based on double-layer fuzzy partitioning and its convergence was proved.The first layer of fuzzy partitioning was applied for state space,the second layer of fuzzy parti-tioning was applied for action space,and Q-value functions were computed by the combination of the two layer fuzzy partitioning.Based on the Q-value function,the consequent parameters of fuzzy rules were updated by gradient descent method.Applying DFP-OPTD on two classical reinforcement learning problems,experimental results show that the algo-rithm not only can be used to get a continuous action policy,but also has a better convergence performance.