Next, let us look into the Q_Learner class's member function definitions. The __init__(self, env) function takes the environment instance, env, as an input argument and initializes the dimensions/shape of the observation space and the action space, and also determines the parameters to discretize the observation space based on the NUM_DISCRETE_BINS we set. The __init__(self, env) function also initializes the Q function as a NumPy array, based on the shape of the discretized observation space and the action space dimensions. The implementation of __init__(self, env) is straightforward as we are only initializing the necessary values for the agent. Here is our implementation:
class Q_Learner(object): ...
