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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 ...


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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 ...


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It looks like your indices for the predicted data are off by one. You could try to fix it with predicted = predicted[1:]


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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 ...


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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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