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You may start assigning penalties for undesirable conditions in a state like: 1) Number of blocks outside stack 0. Supose you penalize with 10 units each block outside stack 0, then the starting state above adds 40 units to the penalty score 2) Number of blocks in the stack 0 in a position different than in the goal state. Supose you penalize with 50 ...


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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 decision point, a separate full action. If you have, for example, already selected 4 sub-actions for a particular customer, you can try to include those in some way ...


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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 ∈ ℝs×a, where s is number of possible states, and a is number of possible actions. In addition to a small overhead of agent's parameters: &...


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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 understanding the task, but it is not mathematically correct. The agent cannot "merge" states because they involve the same behaviour. It must count ...


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This is more of a combinatorics than AI question but regradless, the full state information for the environment is: $(taxi \space position, passenger \space position, destination \space position)$ There are 25 possible taxi positions, 5 passenger positions and 4 destination positions making it $25 \cdot 5 \cdot 4 = 500$, so the paper is correct. You ...


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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 domain where there is an infininite amount of possibilities of placing an object in this space. Let's call the position of the object its state. Constraining the ...


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