This paper presents a new continuous action space for reinforcement learning (RL) with the wire-fitting . The wire-fitting has a desirable feature to be used with action value function based RL algorithms. However, the wire-fitting becomes unstable caused by changing the parameters of actions. Furthermore, the acquired behavior highly depend on the initial values of the parameters. The proposed action space is expanded from the DCOB, proposed by Yamaguchi et al. , where the discrete action set is generated from given basis functions. Based on the DCOB, we apply some constraints to the parameters in order to obtain stability. Furthermore, we also describe a proper way to initialize the parameters. The simulation results demonstrate that the proposed method outperforms the wire-fitting. On the other hand, the resulting performance of the proposed method is the same as, or inferior to the DCOB. This paper also discuss about this result.