New answers tagged proofs
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There are three problems
Limited capacity Neural Network (explained by John)
Non-stationary Target
Non-stationary distribution
Non-stationary Target
In tabular Q-learning, when we update a Q-value, other Q-values in the table don't get affected by this. But in neural networks, one update to the weights aiming to alter one Q-value ends up affecting other Q-...
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Branch and Bound is similar to an exhaustive search, except it incorporates a method for computing lower bounds on branches. If the lower bound on a given branch is greater than the upper bound on the problem (i.e. the current best solution encountered), that branch can be discarded since it will never produce an optimal solution.
Hence, since you explore ...
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Not really, I mean at it's core machine learning from an application perspective often seeks produce human level results, but there isn't any theorem describing human understanding of reality.
Like proving computer vision works well is essentially like proving you have a correct understanding of human perception.
It becomes somewhat circular, and while ...
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I will try to answer the question in a lesser mathematical (and hopefully correct way).
NOTE: I have used $V_{\pi}$ and $v_{\pi}$ interchangeably.
We start from LHS:
$$\max_s \Bigl\lvert \mathbb{E}_{\pi} \left[ G_{t:t+n} \mid S_t = s \right] - v_{\pi}(s) \Bigr\rvert$$
This can be written in terms of trajectories. Say the probability of observing a $n$ ...
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