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A comment on How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos. Parent comment

Re:How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fak... by bitL on Thursday January 04, 2018 @08:02PM (#16075609)
RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple. It requires huge amount of tries/episodes and properly tuned exploration-exploitation ratio. It is somewhat based on biological reinforcement learning, so there might be basis in reality as it is with convolutional neural networks and visual field maps in visual cortex (even if very rough approximation). DRL is the technique that allows modeling decisions; so for predictions you have CNN/RNN/FCN, for generation GANs and for decisions DRL; together they are closest to AGI we have right now.
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Re:How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fak... by halflings on Thursday January 04, 2018 @08:46PM (#16075827)
> RL/DRL assumes world is Markovian, i.e. past doesn't matter between two states, which is way too simple.

There's plenty of RL papers using RNNs and some types of memory networks.

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Re:How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fak... by bitL on Thursday January 04, 2018 @09:27PM (#16075996)
Likely as value function approximators for one piece of the whole algorithm (as is the case with DQN/DDQN). However the main algorithm is likely using variation of Bellman equation, that assumes Markovian property and gives strong guarantees about convergence.
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Re:How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fak... by gwern on Thursday January 04, 2018 @11:46PM (#16076625)
If you're using DQN or pretty much anything in DRL, you don't have any guarantees about convergence in the first place, and using a RNN does give you the history summary you need (at least up to the minimum error achievable with that fixed-length summary, not that that is any more likely to converge than the overall DRL algo is).
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Re:How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fak... by bitL on Friday January 05, 2018 @10:26AM (#16079125)
I meant that under Markovian assumption value iteration used for Bellman equation is guaranteed to converge. So it makes math people happy, even if such property doesn't hold in the real world nor in the problem they try to solve, and the "deep" in DRL is just heuristics, though surprisingly working in many cases.
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