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2 votes

Model-based learning in continuous state and action spaces

You can use function approximation like neural networks to learn the whole environment, i.e. both the transition function, $p(s'\mid s, a)$, and the reward model, $r(s,a,s')$: $$p(s',r\mid s,a)$$ In ...
Luca Anzalone's user avatar
1 vote

Variable observation space at each episode

Actually, in most of these algorithms, that state is just used as input for some functions (e.g. some value or policy functions). Given the correct class of functions (e.g. recurrent neural networks), ...
Broele's user avatar
  • 561

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