# AI News, Improved Q-learning with continuous actions

- On Sunday, June 3, 2018
- By Read More

## Improved Q-learning with continuous actions

But we can also think of a neural network as of a high-dimensional manifold in the infinite-dimensional space of all possible functions, and we can, at least conceptually, run gradient descent in function space, subject to the constraint that we stay on the neural network manifold.

Thus, the choice of sigmoid versus tanh will affect the backpropagation algorithm, but it will not affect the idealized natural gradient, since natural gradient depends entirely on the neural network manifold, and we already established that the neural network manifold is unaffected by the choice of sigmoid versus tanh.

But the relevant fact about natural gradient is that its behavior is much more stable and benign in a variety of settings (for example, natural gradient is relatively unaffected by the order of the data in the training set, and is highly amenable to data parallelism), which suggests that natural gradient could improve the stability of the Q-learning algorithm as well.

- On Sunday, June 3, 2018
- By Read More

## Improved Q-learning with continuous actions

But we can also think of a neural network as of a high-dimensional manifold in the infinite-dimensional space of all possible functions, and we can, at least conceptually, run gradient descent in function space, subject to the constraint that we stay on the neural network manifold.

Thus, the choice of sigmoid versus tanh will affect the backpropagation algorithm, but it will not affect the idealized natural gradient, since natural gradient depends entirely on the neural network manifold, and we already established that the neural network manifold is unaffected by the choice of sigmoid versus tanh.

But the relevant fact about natural gradient is that its behavior is much more stable and benign in a variety of settings (for example, natural gradient is relatively unaffected by the order of the data in the training set, and is highly amenable to data parallelism), which suggests that natural gradient could improve the stability of the Q-learning algorithm as well.

- On Monday, September 23, 2019

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