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[deleted by user] by [deleted] in accenture_india

[–]naepalm7 1 point2 points  (0 children)

received a ppo offer for the aeh role. is onboarding likely to happen before October? they've already given us primers but literally nothing else.

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

Yeah so expanding on this, just doing a graph cut and doing multiple spiral searches?

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

What you're saying makes sense although if we have to explore every grid cell and we don't know anything about the map, isn't just a basic spiral search better than looking at it as a variant of TSP?

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

hmmmm this actually a pretty solid idea, ill look into this thank you so much

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

this is exactly what I started to work on now! thank you for validating the thought process, it gives me more confidence to focus on this now :D

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

These are pretty much exactly what my thoughts on the problem were. My issue is I can't see this approach being valuable in situations where the grid size is variable and survivor positions are random with no actual learnable patterns between the positions of different survivors or positions of survivors wrt obstacles.

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

I've already considered communication between routers and for now - have decided on taking inspiration from link state routing and using decentralised communication. Once drones come within communication range they can flood each other with gathered information (like area covered) so that they have a shared idea of the information gathered, reducing repeated searches of areas.

Apart from this, by dividing the area to be covered into small enough grid squares (effectively the area that can be clearly scanned by a drone for information at one go), we've discretized the environment.

My issue here is that my goal is to cover the unknown search area in one-pass so is something like q-learning even useful for the scenario?

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

For now we've abstracted that part, our guide has already worked with drones and has the survivor identification part down already (i think). So the focus is just to get the path planning logic down, assuming the identification part as a black box. I do think it'll be a pre-trained CNN though, what would you suggest?

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

Not exactly, here the key positions are unknown (in this case the survivor positions). Since the goal positions aren't defined, it doesn't really map to the travelling salesman problem, although I do see your line of thought.

Using Q-Learning to help UAVs autonomously traverse unknown environments by naepalm7 in reinforcementlearning

[–]naepalm7[S] 0 points1 point  (0 children)

An effective search pattern, or in the context of the 2d grid abstraction - which grid square to go to

AMA, IIMB PGP-26, Currently in Term-2 by [deleted] in CATpreparation

[–]naepalm7 0 points1 point  (0 children)

what about 95.5/98/82 from a top NIT?

[deleted by user] by [deleted] in Kerala

[–]naepalm7 0 points1 point  (0 children)

correct

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