Before revision:  However, because of the complexity of the models, the bound gait built by the conventional control method is often unnatural and slow to compute. In the present work, we introduce a method to achieve the bound gait based on model-free pre-fit deep reinforcement learning (PF-DRL) . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
After revision:  However, due to a large number of robot and environmental constraints, conventional planning and control has limited ability to adapt bounding gaits on various terrains in real-time. We proposed an efficient approach to learn robust bounding gaits by first pretraining the deep neural network (DNN) using data from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .