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Let's say your old policy is $\pi_b$ and your current one is $\pi_a$. If you collected trajectory by using policy $\pi_b$ you would get return $G$ whose expected value is \begin{align} E_{\pi_b}[G_t|S_t = s] &= E_{\pi_b}[R_{t+1} + G_{t+1}]\\ &= \sum_a \pi_b(a|s) \sum_{s', r} p(s', r|s, a) [r + E_{\pi_b}[G_{t+1}|S_{t+1} = s']]\\ \end{align} You can ...


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The correct number of child processes will depend on the hardware available to you. Simplifying a bit, child processes can be in one of two states: waiting for memory or disk access, or running. If your problem fits nicely in your computers' memory, then processes will spend almost all of their time running. If it's too big for memory, they will ...


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Notably, these two tips/tricks are useful because we are assuming the context of deep reinforcement learning here, as you pointed out. In DRL, the RL algorithm is guided in some fashion by a deep neural network, and the reasons for normalizing stem from the gradient descent algorithm and the architecture of the network. How does this affect training? An ...


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The question is conceptually wrong, because of misunderstanding of area. Explanation: The idea is to replace open ai gym by something different. For example: web-site or computer game. There is no way to create an environment based on image. If you want to use implemented algorithm for open ai gym and want to change environment for your own, could do ...


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Disclaimer: Without the full code, we can only speculate. I encourage you to post the full code on Google Colab or something like this. In the meanwhile, here is my point of view: The Problem Looks like your model has found some "master action" that always leads to zero loss, no matter what the state is. So it's not necessarily bad, it's just ...


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