Overall RL control framework.

A random initial state is fed into the algorithm at the start of each episode and two neural networks (CPN and MCN) are used to control the MSK model. RL rewards are computed to update the CPN, and a supervised loss function is used to update the MCN. The random initial state is given via selected ankle position and velocity determined by the Reference State Initialization (RSI) algorithm.