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Both your notation and terminology are quite confusing. For example, I'm not sure what is an "optimal" Bellman operator is. Here's a good clarification on definition of a Bellman operator. Likewise, your description of the DQN algorithm completely ignores the averaging over states/actions/rewards sampled from the replay memory. Trying to savage ...


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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 $$\...


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After working on it for a while this is what I got. Concerning proposition 1 in the paper, a rigorous statement could be the following version of the Gradient Theorem for line integrals: Proposition 1. (Gradient Theorem for Lipschitz Continuous Functions). Let $U$ be an open subset of $\mathbb{R}^n$. If $F : U \to \mathbb{R}$ is Lipschitz continuous, and $\...


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