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A layer with bigger number of nodes than previous one is something very common. Some examples are:
strategies encoder-decoder (autoencoders) where the encoder typically has layers with a decreasing number of nodes (until the compressed/encoded data) and the decoder has layers increasing in number of nodes.
bidirectional recurrent networks where in the ...
4
Before anything, the function you have wrote for the network lacks the bias variables (I'm sure you used bias to get those beautiful images, otherwise your tanh network had to start from zero).
Generally I would say it's impossible to have a good approximation of sinus with just 3 neurons, but if you want to consider one period of sinus, then you can do ...
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In some sense, you're right that a neural net is just another tool to fit data. However, it's quite the tool! There's this universal approximation theorem saying that, under decent conditions, a neural network can get as close as you want to a wide class of functions. This means that you can get the network to give you complicated shapes with squiggles all ...
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The state space is certainly continuous, assuming that you can somehow feed that AI exact coordinates. You may have to resort to CNNs if you do not have access to this information. For the action space, you should consider how the game actually plays. Since you use a mouse to simply show the direction, you could use (x,y) positions of the mouse as an action, ...
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You do not always need a fitness function to perform genetic algorithm searches. The more general need is for a selection process that favours individuals that perform better at the core tasks in an environment. One way of assessing performance is to award a score, but other approaches are possible, including:
Tournament selection where two or more ...
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Yes, there are different ways. What I think you are looking for is under the research field of Localization and Mapping. Which divides in the following subfields:
For getting current (the robot) position and trajectory go to models for Odometry Estimation
For getting a representation of the world around the robot go to models for Mapping
If you want both of ...
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As you say, the outputs are modeled as a vector, each output in one vector component.
In regression problems:
The most common loss function, like in the scalar case, is the square error. Skipping constants, it is defined as:
$$E=\sum_i ||\mathbf{y_i}-\mathbf{\hat{y_i}}||^2 = \sum_i (\mathbf{y_i}-\mathbf{\hat{y_i}})(\mathbf{y_i}-\mathbf{\hat{y_i}})$$
where:
$...
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Assuming you're using softmax on the last layer for classification, it sounds like a simple application of cross entropy loss from here on out: https://datascience.stackexchange.com/questions/20296/cross-entropy-loss-explanation
Edit:
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Turns out the reason is because, for places where a dot is shown in the image above, they're actually element-wise multiplications, not dot products. A lot of sources use an X or . to denote multiplication, but don't clearly indicate they mean element-wise multiplication.
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It is not clear form your question, how you use your replay buffer. Basically, you have to store all states/reward tuples and train your agent on the entire buffer.
Moreover, you should give the agent time to explore (all) states of the world. But if you want to speed up training, you can try to implement importance sampling
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