In Reinfrocement Learning (RL) in Neural Networks (NNs), I've seen two approaches to Q-learning.
The first is to tile the state space with basis functions using Spiking Neural Networks (SNN) to represent reward. This approach is used in "A neural reinforcement learning model for tasks with unknown time delays" by Daniel Rasmussen and is expanded upon in "A neural model of hierarchical reinforcement learning" by the same author.
The second is to use Deep Neural Networks (DNN) to map the state space to the reward space, such as the various publications used by Deep Mind.
From what I've read so far, I believe the differences between these two approaches are as follows:
- SNNs take less long to train
- SNNs can be deployed on existing neuromorphic hardware
- SNNs are more easily extended into continuous cases
- SNNs do more inference between states
- DNNs can map larger and higher dimensional state spaces
- DNNs require less knowledge of the task for the programmer
Is this analysis accurate? Are there other differences that I've missed?