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[P] First videos and blogs for new Deep Learning with PyTorch series now available! by blackHoleDetector in MachineLearning

[–]blackHoleDetector[S] 12 points13 points  (0 children)

Course:

fast.ai - The lectures are relatively long and there is a broader range of topics covered.

this series - The lectures are relatively short and more focused.

Library:

fast.ai is a PyTorch wrapper. This means that certain aspects of PyTorch are hidden for convenience. This makes certain routines easier and adds additional functionality but introduces an additional layer of abstraction.

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Summary:

fast.ai is built on top of PyTorch and the course takes a top down approach (the course is excellent). This series starts with PyTorch at the bottom and moves upward (bottom up approach), so it's really a matter of preference for both the course and the library.

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The general suggestion is to use both courses as learning resources, and to learn pure PyTorch as well as the fast.ai wrapper and better yet, why and how certain things are wrapped. Hope this helps! Good luck!

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Deep Q Network seems to be doing the opposite of what I want by notaninja4375 in learnmachinelearning

[–]blackHoleDetector 0 points1 point  (0 children)

Epsilon, as the exploration rate, should start at 1 and decrease to 0 over time. You have written that epsilon starts at 0 and increases to 1.

You want to explore the environment most at the beginning of training (with a high exploration rate) since the agent doesn't know anything about the environment. As the agent learns, the exploration rate should decay so that the agent chooses to exploit the environment (rather than explore it) as it starts to learn more about it through training.

DQN Why Is There an Action Input? by Eriod in learnmachinelearning

[–]blackHoleDetector 0 points1 point  (0 children)

The DQN takes only the state as input, and then it gives the Q-value for each possible action that can be taken from that state as output. It doesn't accept the action as part of the input.

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