The result of pq_A* and differentiable A* looks same.
speedhawk opened this issue · comments
Hi! During running the codes, I advertantly noticed that the contrastical experiments for neural A* is not standard A* but called differentiable A* which is really novel for me. However, after I change it into standard A* (just as the content said, I changed all of use_differentiable_astar variable into false), I found that standard A* returned an identical result as differentiable A*.
I have to say that I am not quite familiar with the algorithm of breadth first searching. Therefore, could you please briefly introduce me about differentiable A* algorithm or recomment me any resource to learn it? Additionally, could you please tell me why the differentiable A* returned the same result as standard A* in these maps? Many thanks and waiting for your reply!
Hi! To get to know neural A*, you may first check out our blog post:
https://medium.com/sinicx/path-planning-using-neural-a-search-icml-2021-ecc6f2e71b1f
Differentiable A* indeed returns identical results as those of standard A*. It is a re-formulation of A* to be performed as a part of neural networks.
Hi! To get to know neural A*, you may first check out our blog post: https://medium.com/sinicx/path-planning-using-neural-a-search-icml-2021-ecc6f2e71b1f
Differentiable A* indeed returns identical results as those of standard A*. It is a re-formulation of A* to be performed as a part of neural networks.
Got it! Very thanks for your enlighting and recommending in this field!