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Vector pointing in the direction where a function is growing fastest; its components are partial derivatives of this function. For questions about gradients in ecology, please use the [ecology] tag instead.

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Need for reparameterization trick in RL (and others)?

The longer answer is (please feel free to correct me if I say something incorrect): The gradient of the RL objective is of the form: $$\nabla_\theta\mathbb{E}_{\tau\sim\pi(\theta)}[r(\tau)]=\nabla_\theta … tau)\nabla_\theta\pi(\theta)d\varepsilon=\mathbb{E}_{\tau\sim \varepsilon}[r(\tau)\nabla_\theta\pi(\theta)]$$ Intuitively, the reparameterization trick allows to separate the parameters with which the gradient
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Need for reparameterization trick in RL (and others)?

theta}=\dfrac{\partial \log \pi}{\partial \mu_\theta}=\dfrac{a-\mu_\theta}{\sigma}$$ 1) Since an action $a$ has been sampled, can't that action $a$ be put back in $\nabla_\theta\log\pi$ an evaluate the gradient … For instance, just plugging in $a,s,\theta$ in the above equation would result in the gradient to update the mean. …
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