Difference between SNN RL and DNN RL?
Daniel Rasmussen replied via email:


  
  SNNs take less long to train
  
  
  This really depends on the training method, and the implementation. 
  If you just implemented an abstract DNN approach to solve the same
  task as is being solved in those NEF RL papers, it would be a pretty
  simple network and would train quite fast (almost certainly faster
  than with the simple PES rule).  On the other hand, if you recreated
  the internal structure of the SNN model, and trained it using a DNN
  approach (e.g., using something like nengo_deeplearning), I suspect
  that that would never converge to something useful.  So overall it's
  hard to say something useful to compare them in this sense, but I
  think if you had to commit to something, the DNN training methods are
  probably more efficient than SNNs.
  
  
  SNNs can be deployed on existing neuromorphic hardware
  
  
  Yep
  
  
  SNNs are more easily extended into continuous cases
  
  
  I'm not sure if this is true.  It is true that there aren't a lot of
  examples of people doing continuous state processing with DNNs, but
  that's also true of SNNs.  I'm not sure it's any easier or harder for
  DNNs than SNNs (it's just harder than the discrete case in both
  paradigms).
  
  
  SNNs do more inference between states
  
  
  Again, don't think this is true in general, it'll all depend on how
  things are implemented.
  
  
  DNNs can map larger and higher dimensional state spaces
  
  
  Also depends on the implementation, but it is true that DNNs tend to
  be more computationally efficient than SNNs (because they're not
  incorporating all those biological features).
  
  
  DNNs require less knowledge of the task for the programmer
  
  
  Not categorically true, but probably true in most cases.