## 1 Introduction  
Biped robots are designed with a physical structure that tries to emulate a human being with the purpose of providing versatility in terms of movement in such a way that these robots can move through irregular terrains and are better adapted in comparison with robots with wheels [1]. This kind of robots is easily adapted to the human environment due their locomotion, and humans can adapt easily to their interaction because of their similarity in terms of physical structure. Given their kinematic complexity (more than twenty degrees of freedom), the methods to control them are highly complex, with this level of complexity and their extensive potential a recurrent theme of study for many researchers, both in the robotics area and the artificial intelligence area. Each field proposes new control methods that offer better velocity of displacement, better stability, or the capacity to walk in different terrains. In this work, we propose a suitable neural architecture in order to reach a fixed distance in the shortest time. Our main goal is, given a set of zero moment point (ZMP) regions, to enable a biped robot to find a convenient gait cycle. We aim to generalize the definitions of the actions, states, and rewards to generate walks showing a stable behavior, i.e., the robot should not fall down. Additionally, the gait cycle must be as fast as possible. There is a large number of articles related to the control of biped robots, many of which propose highly complex techniques achieving good performance [2; 3; 4]. Recently, several works use machine learning techniques, such as reinforcement learning (RL), to achieve control of the gait cycle of a biped robot [5; 6]. Because of this, the goal of this work is to develop a learning system that allows a biped robot to perform the walking cycle with the aim to improve its performance. Conventional control methods of the biped robots involve many parameters such as the length of the robot, its weight, the speed at which the engines respond, its center of mass or its center of gravity, among others. That is, the design of a control strategy is specific for a robot, with particular parameters for that robot, whereby a strategy that is originally designed for robot X will work in robot Y only if the parameters of robot Y are the same or at least similar to the ones in X; however, if the parameters of robot Y are different, the control strategy that was used in robot X will not be applicable to robot Y and a new control has to be designed, this time considering only robot Y. We are talking about strategies that even when they work, they are not flexible, and attempting to generalize them becomes a very complicated task. Having said this, the effort to develop a control strategy that is versatile enough to be applicable in different robots without any substantial modification of its structure makes sense and is precisely in that niche that our proposal is born: the idea of using self-learning algorithms to overcome such limitations. In this work we opt for the reinforced learning method because the way it works is very similar to the way humans learn, that is, through trial and error. Due to the number of experiments required to obtain a practical set of movements, the ideal environment for implementing such a method is a simulated environment, following with the idea since the inception of simulators, that they should allow a straightforward transfer to real robots [7]. The use of simulators to avoid costly testing of new ideas and improve efficiency is a common practice amongst participants of the DARPA (Defense Advanced Research Projects Agency) Robotics challenge [8; 9]. The structure of the rest of this paper is as follows. In Section 2, we present a compilation of some recent works about the control of a biped robot; it is divided in three sub chapters, each one corresponding to a different approach: classical, ZMP, and alternative approaches.