
Deep Reinforcement Learning: An Overview - gwern
https://arxiv.org/abs/1701.07274
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evc123
What important DRL stuff does this not mention?

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deepnotderp
That DRL is incredibly difficult to stabilize in general.

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kleiba
More details, please. I'd especially be interested how the "in general"
insight has been derived.

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deepnotderp
Basically, when deep reinforcement learning works, it's like magic, but unlike
supervised learning where the default expectation is that it works without a
hitch right out of the box, the default expectation for most new tasks through
deep reinforcement learning is that it will fail, and you will need something
to fix it.

For example, the high dimensionality of robotics makes it very difficult to
apply deep reinforcement learning to it, although it definitely can and has
been applied (and is, IMO, the future of robotics).

Another example is that simple supervised learning often outperforms DRL for
many arcade games.

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jellyksong
What is the current state of the art for multi-agent DRL?

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TheArcane
>arxiv.org refused to connect.

