Andrew Ng, Daishi Harada, Stuart Russell published a conference paper entitled Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping.
There is a specific example there that I am extremely curious/interested about. It is in Figure 2(a) of the paper:
It is about a 5x5 gridworld with start and goal states in opposite corners. The catch is, the agent must learn to go from start to end BY VISITING specific subgoals 1,2,3,4 IN ORDER.
Has anyone seen/understood the code for this? I want to know how the reward function/shaping is given in this kind of problem.
I am interested to know how the flow of this modification to the grid world is written.