Erik Laukien is back with a demo of Sparse, Distributed Representation with Reinforcement Learning.
This topic is of intense interest to us, although the problem is quite a simple one. SDRs are a natural fit with Reinforcement Learning because bits jointly represent a state. If you associate each bit-pattern with a reward value, it is easy to determine the optimum action.
However, since this is an enormous state-space, it is not practical to do so. Instead, one might associate only all observed bit patterns with reward, or cluster them somehow to reduce the number of reward values that must be stored. Anyway, these are thoughts for another day.
Here’s his explanation of the demo:
Here’s the demo itself. Note, we had to set the stepsPerFrame parameter to 100 to get it working quickly.