0
$\begingroup$

I am working on a project to implement a collision avoidance algorithm on a real UAV. I'm interested in understanding the process to set up a negative reward to account for scenarios wherein there is a UAV crash. This can be done very easily during simulation(if the UAV touches any object, the episode stops giving a negative reward). Any ideas will be highly appreciated.

$\endgroup$
0
$\begingroup$

From what I understand this is quite easy and you don't have to use reinforcement learning. You can also use standard ANNs.

Algorithm:

For as long as you want:
If Touch (sensor. Maybe a button???) == 1
Update network (the network should output the probability of crashing in the next step. You can also use markov chains for multiple steps (just an idea))

I hope this answers your question.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.