Yes, I think this is pretty much the case for PyTorch vs Etaler. But more interesting is the fact that, while the RNN is “defeating itself”, the HTM system is doing spectacularly well at predicting the RNN’s moves (i.e. the RNN is more predictable than a RNG). HTM is clearly better suited for predicting how an RNN will model it, than an RNN is at predicting how HTM will model it.
It is also important to note that these experiments do not indicate RNN is simply unable to play RPS. RNN vs RNN for example shows a clear drop in the draw rate:
One of the other experiments from the previous round (tiny-dnn VS NuPIC.core) I think also supports the idea that HTM is just learning and updating its model faster than the RNN can keep up with. As soon as HTM learning was turned off, the RNN had a chance to accurately model it. Prior to that, it was just too fast of a moving target.