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It looks like the inverse reinforcement learning problem defined by Stuart Russell as
Given
measurements of an agent’s behaviour over time, in a variety
of circumstances.
measurements of the sensory inputs to that agent;
a model of the physical environment (including the agent’s
body).
Determine the reward function that the agent is optimizing.
It is ...
1
No, assuming your input vectors are one-hot encodings. These input one-hot encodings are in an $n$-dimensional Euclidean vector space. The last hidden layer of an LSTM is not due to the non-linear activation functions across the encoder. Therefore, an average of the inputs will not necessarily align well in a vector space with the model output, nor are you ...
1
It looks like your indices for the predicted data are off by one. You could try to fix it with
predicted = predicted[1:]
1
There are a couple of options:
Optimize tensorflow for your specific CPU. Sometimes the official versions of tensorflow are not compiled with support for some instruction sets (e.g. SSE4.1, SSE4.2, AVX, AVX2, FMA). Usually, there is a tensorflow runtime warning message stating so. This prevents some computations to take place in parallel. You can either ...
1
the syntax arr[:,:-1] selects all rows and every column except the last one. Python can use negative indexing, but it's inclusive-exclusive such as [a,b): inclusive of a, exclusive of b.
If you don't use the : operator, such as arr[:,-1], then it simply selects the entire last column.
So in the context of your example, the last column is the value to be ...
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