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Iteratively and adaptively increasing the network size during training

At DeepChess they trained stacked autoencoders: Training Pos2Vec: We first trained a deep belief network (DBN), which would later serve as the initial weights for supervised training. The DBN is ...
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How many training steps does it usually take to train an RL model?

It depends on the the problem you're applying PPO to. To get an idea, you can have a look at the CleanRL benchmarks, there are a few of them where they use PPO: https://wandb.ai/openrlbenchmark/...
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1 vote

How many training steps does it usually take to train an RL model?

This is not possible to know in advance precisely, only approximately, but it also strongly depends on the environment, hyperparameters and algorithm. For hard environments, e.g. the ones learning ...
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What kind of NN I need to find ideal ranges and correlation between them?

What you described seems like a pretty standard binary classification problem. There are many good algorithms, that are much simpler and more interpretable than NNs. I don't see why you would straight ...
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How to explain near zero gradients on first epochs?

Nope. Delta rule: taylor series with truncation relates mean, variance, and gradient. Variance is proportional to the square of the local slope in the region. If your minimum is at the bottom of a ...
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Why do we train the discriminators k times but train the generator only 1 time in a iteration in GAN?

The answer to your question can be found in [1, sec. 4.4]. Briefly, the GAN optimization problem is a mini-max game, and early on the proposition of GANs, the authors had the idea that one should ...
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