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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
2 votes
Accepted

why learn an observation model when training latent space model in model based rl

The problem is the definition of what's $o_t$ and $s_t$: $o_t$ is the (partially) observable part $s_t$ is ideally a perfect model of the world now, by definition, you don't have access directly to $...
Alberto's user avatar
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2 votes
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In Q-Learning the Q-Table is not considered a model of the game?

The Q table is a useful summary of the underlying Markov Decision Process (MDP) model description of the environment and available choices. A Q table summarises expected results for a single policy - ...
Neil Slater's user avatar
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1 vote

In Q-Learning the Q-Table is not considered a model of the game?

The key is that Q-learning does not use $p(s' \mid s, a)$ - the transition model - in its standard formulation. The Q-table isn't this model. It's not even an estimate of this model because it doesn't ...
nbro's user avatar
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1 vote

In Q-Learning the Q-Table is not considered a model of the game?

No, a model in RL is considered something (you can even consider it as a black box) that knows how to transition from one state to the next one, so for example, if we are playing snake, if I give you ...
Alberto's user avatar
  • 2,293

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