Let's balance exploration and goal-directed traversal (AIMA ch. 4).

Last week we demonstrated that a randomly exploring agent can score net-positive in the partially observable world; let's try to maximize our score this time through exploration and goal-directed traversal.

One policy might be to randomly explore until a known path is encountered, and then follow that path to the goal; one can imagine a situation, however, in which it might be better to reject a suboptimal path and keep exploring.

We might be able to use a simulated annealing model where the probability of rejecting known paths for random exploration decreases with time.

I'm sure there are other interesting solutions, too.

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  • Peter D.

    Good conversation; a little non-functioning code.

    February 21, 2013

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