Since most policies depend solely on actions and states/observations, then if you model the space of this field as the Cartesian Product of your state and action spaces, then the policy learns a surface over this combined space, similar to the way a field is parameterized.
The policy an agent learns could exhibit the same behavior as the field you describe above (obstacles form an repulsive field, and goal(s) form an attractive field). However, unlike the field described above, it is not guaranteed that the learned policy will capture this behavior - the policy learned depends on:
- The learning algorithm used
- The approximators (e.g. neural networks) used for learning, and their respective hyperparameters
- The formulation of the reward function
- The number of episodes/total steps the policy/agent is trained over.
To sum this answer up, I believe you could train the policy in such a way (using the mechanisms above) such that it resembles the field you describe.