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When training a Deep Q network with experienced replay, you accumulate what is known as training experiences $e_t = (s_t, a_t, r_t, s_{t+1})$. You then sample a batch of such experiences and for each ...

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In Model Based Reinforcement learning, state and state-action values for all states can be calculated based on the bellman equations. The equations are taken from Andrew Ng's Algorithms for Inverse ...

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All right, I figured it out. trajectories need not have the same starting state because the distribution of $s_0$ is drawn from a distribution D (mentioned in the paper). Had been confused because ...

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$\frac{p(s', r \mid s, a)}{p(s' \mid s, a)}$ represents the probability of observing reward $r$ in state $s'$, given that state $s'$ is the next state transitioned to. The equation assumes a ...

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Regarding your first question, $$V^{\pi}(s) = \sum_{a \in A}\pi(a|s)Q^{\pi}(s,a)$$ so recovering the value function from Q really depends on what policy $\pi$ you are using. Hence, you can't really ...

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I will answer the first question question based on information I have gathered so far. The probability of each action for the $\textbf{two player zero sum game}$ need not be the same for both players. ...

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The loss function is designed in a way to approximate the bellman optimality for $Q^*(s,a)$. Given an optimal policy $\pi^*$, $Q^*(s,a)$ satisfies the equation Q^*(s,a) = r(s) + \gamma max_{a'}\sum_{...

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Inverse Reinforcement Learning (IRL) is a technique that attempts to recover the reward function that the expert is implicitly maximising based on expert demonstrations. When solving reinforcement ...

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When we use our network to approximate our Q values,is the Q target a single value? Yes, the target Q value is a single value if you are just updating a single training example. The loss function of ...

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In generalised Linear models, each output variable $y_i$ is modelled as a distribution from the exponential family, with the hypothesis function $h_{\theta}(x)$ for a given $\theta$ as the expected ...

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I guess the gradient of the expectation of the Utility function, $\nabla_{\theta}J(\theta)$ in policy gradient methods where $\nabla_{\theta}J(\theta) = E_{\tau \sim p(\tau ; \theta)}[r(\tau)\nabla_{\... View answer 2 answers 1 votes 80 views 0 votes In VAE's, we want to model the distribution of images$x$with some latent variable$z$. Because$x$is a random variable, You can think of$P(x|z)$as the distribution of images$x$conditioned on ... View answer 3 answers 1 votes 148 views Accepted answer 0 votes I'm not sure how just training the batch normalisation layer, you can get an accuracy of 83%. The batch normalisation layer parameters$\gamma^{(k)}$and$\beta^{(k)}$, are used to scale and shift the ... View answer 2 answers 1 votes 147 views 0 votes The grad log probability of the trajectory parameterised by$\theta$tells us the direction$\theta$should move to increase the probability of that trajectory$P(\tau;\theta)\$ the most. If the ...

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