rl | Alternative Python bindings for GNU Readline

As we talked about in the comments, it seems that the Keras-rl library is no longer supported (the last update in the repository was in 2019), so it's possible that everything is inside Keras now. I take a look at Keras documentation and there are no high-level functions to build a reinforcement learning model, but is possible to use lower-level functions to this.

• Here is an example of how to use Deep Q-Learning with Keras: link

Another solution may be to downgrade to Tensorflow 1.0 as it seems the compatibility problem occurs due to some changes in version 2.0. I didn't test, but maybe the Keras-rl + Tensorflow 1.0 may work.

There is also a branch of Keras-rl to support Tensorflow 2.0, the repository is archived, but there is a chance that it will work for you

By creating a local variable to track whether you've already encountered the first record, you can prepend the comma to all entries after the first one, achieving the same effect:

Note that, because whitespace is insignificant in JSON, you will decrease the size of the resulting file by omitting the newlines

I will follow the same order in which you introduced your problems.

1. writing-mode which you're using on the titles, cannot be animated. You could try rotating the text instead using the transform property
2. If you want to make the whole grey area clickable, you should move both the onClick function and the cursor: "pointer" property to the parent div of that exact element which is the title.

Essentially, what happens here is that the output of the net is being sliced to get the desired part of the Q table.

The (somewhat confusing) index of [np.arange(0, self.batch_size), action] indexes each axis. So, for axis with index 1, we pick the item indicated by action. For index 0, we pick all items between 0 and self.batch_size.

If self.batch_size is the same as the length of dimension 0 of this array, then this slice can be simplified to [:, action] which is probably more familiar to most users.

A mock results in rejected promise which isn't handled. The test should be asynchonous and return a promise, i.e. be async. try..catch cannot handle it in synchronous function.

Since the promise is rejected at the time when mockedReadStream.emit is called, it needs to be chained with catch shortly after the promise is rejected, e.g. through Jest promise assertion:

You can dynamically return new functions via lambda expressions:

Short answer:

Change your output vector to Float32 i.e. Float32[-.1, 0, .1].

Long answer:

Crux creates a Distribution over your network's output values, and at some point (policies.jl:298) samples a random value from it. It then converts this value to a Float32. Later (utils.jl:15) it does a findfirst to find the index of this value in the original output array (stored as objs within the distribution), but because the original array is still Float64, this fails and returns a nothing. Hence the error.

I believe this (converting the sampled value but not the objs array and/or not using approximate equality check i.e. findfirst(isapprox(x), d.objs)) to be a bug in the package, and would encourage you to raise this as an issue on Github.

I found an answer to my question, maybe it will help someone. Working with negative values (e.g. Tmin in C), the normal process in GEV analysis is to convert them by multiplying with * (-1), such that positive records are analyzed. Also, you can convert them to Kelvins. Once the GEV is fitted, you can easily back transform the result.