Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 Arrows indicate strength between two problem states Start maze … Reinforcement learning example.

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Presentation transcript:

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 Arrows indicate strength between two problem states Start maze … Reinforcement learning example

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 The first response leads to S2 … The next state is chosen by randomly sampling from the possible next states weighted by their associative strength Associative strength = line width

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 Suppose the randomly sampled response leads to S3 …

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 At S3, choices lead to either S2, S4, or S7. S7 was picked (randomly)

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 By chance, S3 was picked next…

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 Next response is S4

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 And S5 was chosen next (randomly)

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 And the goal is reached …

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 Goal is reached, strengthen the associative connection between goal state and last response Next time S5 is reached, part of the associative strength is passed back to S4...

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 Start maze again…

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 Let’s suppose after a couple of moves, we end up at S5 again

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 S5 is likely to lead to GOAL through strenghtened route In reinforcement learning, strength is also passed back to the last state This paves the way for the next time going through maze

Start S2S2 S3S3 S4S4 S5S5 Goal S7S7 S8S8 The situation after lots of restarts …