Maintaining the stability of bipedal walking remains a major challenge in humanoid robotics, primarily due to the large number of hyperparameters involved and the need to adapt to dynamic environments and external disturbances. Traditional methods for determining these hyperparameters, such as heuristic approaches, are often time- consuming and potentially suboptimal. In this thesis, we present an integrated approach combining advanced control and reinforcement learning techniques to improve the stability of bipedal walking, particularly in the face of ground disturbances and speed variations. Our main contribution lies in the integration of two complementary approaches: (1) an intrinsically stable model predictive control (IS-MPC) combined with whole-body admittance control, and (2) a reinforcement learning module implemented in the mc_rtc framework. This system allows for continuous mo...