Question
Looking at the dense reward function for Maze Env:
return np.exp(-np.linalg.norm(desired_goal - achieved_goal))
The agent seems to prefer sitting the ball as close as possible to the goal without touching it after optimisation.
This makes sense given there is no bonus for reaching the reward and the reward is positive for all time steps.
Why is the dense reward formulated this way?
Question
Looking at the dense reward function for Maze Env:
return np.exp(-np.linalg.norm(desired_goal - achieved_goal))
The agent seems to prefer sitting the ball as close as possible to the goal without touching it after optimisation.
This makes sense given there is no bonus for reaching the reward and the reward is positive for all time steps.
Why is the dense reward formulated this way?