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1

There is no sign error and we should not change to $\arg\max$. With Policy Gradients I find that it is not useful to think about things such as a 'loss'. In short, we want to first find the derivative of the RL objective $J(\theta) = v_\pi(s_0)$, where $\pi$ is our policy that depends on some parameters $\theta$. The policy gradient theorem tells us that $$\...


1

They are not maximizing the gradient, the gradient is of the form \begin{equation} \nabla_{\theta} J \approx \sum_{t=0}^T G_t \nabla_{\theta} \log(\pi_{\theta}(a_t|s_t)) \end{equation} that means that when implementing it in software you can form your objective as \begin{equation} J = \sum_{t=0}^T G_t \log(\pi_{\theta}(a_t|s_t)) \end{equation} and then ...


2

generally the approach is to have a separate head. For example, imagine you have latent vector $z_k$, you would output two values: $h(z_k)$ and $f(z_k)$ where $0 \leq h \leq 1$ and $b_0 \leq f \leq b_1$ where $b_0$ and $b_1$ are your bounds. In thios setup, during inference you would check $h_k$ and if its greater than some threshold (usually .5), youd ...


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