In the following section, we test the capacity of this learning rule for driving the neural controller of an embodied agent towards a critical point. In order to do so, we need to take into account the environment during learning. If we consider two interconnected Ising models (one being the neural controller and other being the environment) Equation 3 holds perfectly if we only apply it to the values of i and j corresponding to units of the neural controller. In our case, we do not use an Ising model as an environment but instead we use two classical examples in reinforcement learning with the goal of testing a more realist scenario. Therefore, our learning rule will be valid as long as the statistics of the environment can be approximated by an Ising model with an arbitrary number of units. Luckily, Ising models in the form of Boltzmann machines are universal approximators21 and the stationary distribution of any arbitrary environment can be approximated by an equivalent Ising model.