
Deep Successor Reinforcement Learning (2016) - aaronjg
https://arxiv.org/abs/1606.02396
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skj
It's not clear to me how this is interestingly different from model-based RL,
where you learn the state function and reward function, and then use various
types of simulation to learn a value function. I guess I'll have to read more
than the abstract...

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noelwelsh
Section 3.2 shows the successor representation (SR) definition. If I'm reading
it correctly the SR might also be described as the discounted stationary
distribution over states.

I haven't seen SR before in the RL literature, but the paper argues that this
representation is useful for sub-goal identification. I guess I'll have to
read more than the abstract as well :)

