3 votes
Accepted

Number of states in taxi environment (Dietterich 2000)

This is more of a combinatorics than AI question but regradless, the full state information for the environment is: $(taxi \space position, passenger \space position, destination \space position)$ ...
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3 votes
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How to generalise over multiple simultaneous dependent actions in Reinforcement Learning

If you want to treat the problem as a full Reinforcement Learning problem, I'd recommend to try avoiding the combinatorial explosion of the action space by treating every sub-action as a separate ...
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  • 9,419
2 votes

Number of states in taxi environment (Dietterich 2000)

This . . . because for the agent it should be the same task to go to a certain point, regardless of whether it's on its way to pick up or to drop-off . . . might seem logical/intuitive to a person ...
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2 votes

What type of reinforcement learning can I do restricted to ~200MB on an average smartphone?

I skimmed through your question and understood that the state/action space is finite, so in this case, RL would be a good option for storage. The most basic RL technique will keep track of a matrix Q ...
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  • 251
1 vote

What is the difference between continuous domains and discrete combinatorial optimization?

A continous domain can be imagined as a space in which the axes of the coordinate systems are the parameters of the continous domain. If we take 2D Cartesian space as an example, it is a continous ...
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