Policy learning refers to mapping an agent state onto an action to maximize reward. A linear policy, such as the one used in the Augmented Random Search paper, refers to learning a linear mapping between state and reward.
When the entire state changes at each time-step, for example in the Continuous Mountain Car OpenAI Gym, the position and speed of the car changes at each time-step.
However, assume we also wanted to communicate the constant position of one or more goals. By "constant", I mean does not change within a training episode, but may change between episodes. For example, if there was a goal on the left and right of the Mountain Car. Are there examples of how this constant/static information be communicated from the environment other than appending the location of the two goals to the state vector? Can static/constant state be differentiated from state which changes with each action?