Most RL courses start with grid world problem like this where robot has to navigate from start to end and RL helps in generating the optimum policy. (State-action pairing).
I am not able to relate this to real robotic (or specifically self driving car application). Most self driving car RL application I have read is obtaining the right acceleration and steering angle given the position of the car relative to its environment. (Based on data from vision sensors). This is very different from this grid-world application. So my questions are
- Is this example, not applicable for self-driving cars or mobile robots?
- If it is applicable, would it be right to say, this is a global planning application where robot find the path at a broad level for static obstacles? Also, this is not a local planning application where dynamic obstacles and robot actions relative to that cane be determined?